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Interactive video retrieval tools developed over the past few years are emerging as powerful alternatives to automatic retrieval approaches by giving the user more control as well as more responsibilities. Current research tries to identify the best combinations of image, audio and text features that combined with innovative UI design maximize the tools performance. We present the last installment of the Video Browser Showdown 2015 which was held in conjunction with the International Conference on MultiMedia Modeling 2015 (MMM 2015) and has the stated aim of pushing for a better integration of the user into the search process. The setup of the competition including the used dataset and the presented tasks as well as the participating tools will be introduced . The performance of those tools will be thoroughly presented and analyzed. Interesting highlights will be marked and some predictions regarding the research focus within the field for the near future will be made.
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Multimed Tools Appl (2017) 76:5539–5571
DOI 10.1007/s11042-016-3661-2
Interactive video search tools: a detailed analysis
of the video browser showdown 2015
Claudiu Cobˆ
arzan1·Klaus Schoeffmann1·Werner Bailer2·Wolfgang H¨
urst3·
Adam Blaˇ
zek4·Jakub Lokoˇ
c4·Stefanos Vrochidis5·Kai Uwe Barthel6·
Luca Rossetto7
Received: 23 December 2015 / Revised: 15 March 2016 / Accepted: 1 June 2016 /
Published online: 23 July 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Interactive video retrieval tools developed over the past few years are emerging
as powerful alternatives to automatic retrieval approaches by giving the user more control
as well as more responsibilities. Current research tries to identify the best combinations of
image, audio and text features that combined with innovative UI design maximize the tools
Claudiu Cobˆ
arzan
claudiu@itec.uni-klu.ac.at
Klaus Schoeffmann
ks@itec.uni-klu.ac.at
Werner Bailer
werner.bailer@joanneum.at
Wol fg an g H ¨
urst
huerst@uu.nl
Adam Blaˇ
zek
blazekada@gmail.com
Jakub Lokoˇ
c
lokoc@ksi.mff.cuni.cz
Stefanos Vrochidis
stefanos@iti.gr
Kai Uwe Barthel
barthel@fhtw-berlin.de
Luca Rossetto
luca.rossetto@unibas.ch
1Klagenfurt University, Universit¨
atstraße 65-67, 9020 Klagenfurt, Austria
2DIGITAL - Institute of Information and Communication Technologies, Joanneum research
Forschungsgesellschaft mbH, Steyrergasse 17, A-8010 Graz, Austria
3Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht,
Netherlands
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5540 Multimed Tools Appl (2017) 76:5539–5571
performance. We present the last installment of the Video Browser Showdown 2015 which
was held in conjunction with the International Conference on MultiMedia Modeling 2015
(MMM 2015) and has the stated aim of pushing for a better integration of the user into the
search process. The setup of the competition including the used dataset and the presented
tasks as well as the participating tools will be introduced . The performance of those tools
will be thoroughly presented and analyzed. Interesting highlights will be marked and some
predictions regarding the research focus within the field for the near future will be made.
Keywords Exploratory search ·Video b rowsing ·Video retrieval
1 Introduction
The Video Browser Showdown (VBS), also known as Video Search Showcase, is an inter-
active video search competition where participating teams try to answer ad-hoc queries in
a shared video data set as fast as possible. Typical efforts in video retrieval focus mainly
on indexing and machine-based search performance, for example, by measuring precision
and recall with a test data set. With video getting omnipresent in regular consumers lives,
it becomes increasingly important though to also include the user into the search process.
The VBS is an annual workshop at the International Conference on MultiMedia Modeling
(MMM) with that goal in mind.
Researchers in the multimedia community agree that content-based image and video
retrieval approaches should have a stronger focus on the user behind the retrieval application
[13,45,50]. Instead of pursuing rather small improvements in the field of content-based
indexing and retrieval, video search tools should aim at better integration of the human into
the search process, focusing on interactive video retrieval [8,9,18,19] rather than automatic
querying.
Therefore, the main goal of the Video Browser Showdown is to push research on inter-
active video search tools. Interactive video search follows the idea of strong user integration
with sophisticated content interaction [47] and aims at providing a powerful alternative to
the common video retrieval approach [46]. It is known as the interactive process of video
content exploration with browsing means, such as content navigation [21], summarization
[1], on-demand querying [48], and interactive inspection of querying results or filtered con-
tent [17]. Contrarily to typical video retrieval, such interactive video browsing tools give
more control to the user and provide flexible search features, instead of focusing on the
query-and-browse-results approach. Hence, even if the performance of content analysis is
not optimal, there is a chance that the user could compensate shortcomings through inge-
nious use of available features. This is important since it has been shown that user can give
4SIRET research group, Department of Software Engineering, Faculty of Mathematics and Physics,
Charles University in Prague, Malostransk´
en
´
am. 25, 118 00 Prague, Czech Republic
5Centre for Research and Technology Hellas, Information Technologies Institute, 6th Klm
Charilaou-Thermi Road, 57001 Thessaloniki, Greece
6Internationaler Studiengang Medieninformatik, Hochschule f¨
ur Technik und Wirtschaft,
Wilhelminenhofstr. 75a, D-12459 Berlin, Germany
7Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, CH-4051
Basel, Switzerland
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Multimed Tools Appl (2017) 76:5539–5571 5541
good performances even with very simple tools, e.g. a simple HTML5 video player [10,12,
42,44].
Other interesting approaches include using additional capturing devices such as the
Kinect sensor in conjunction with human action video search [32], exercise learning in the
field of healthcare [20] or interactive systems for video search [7]. In [7] for example, an
interactive system for human action video search based on the dynamic shape volumes is
developed – the user can create video queries by posing any number of actions in front of
a Kinect sensor. Of course, there are many other relevant and related tools in the fields of
interactive video search, video interaction, and multimedia search, which are however out
of the scope of this paper. The interested reader is referred to other surveys in this field,
such as [34,46,47].
In this paper we provide an overview of the participating tools along with a detailed
analysis of the results. Our observations highlight different aspects of the performance and
provide insight into better interface development for interactive video search. Details of the
data set and the participating tools are presented, as well as their achieved performance
in terms of score and search time. Further, we reflect on the achieved results so far, give
detailed insights on the reasons why specific tools and methods worked better or worse,
and subsume the experience and observations from the perspective of the organisers. Based
on this, we make several proposals for highly promising approaches to be used with future
iterations of this interactive video retrieval competition.
The remainder of the paper is organized as follows. Section 2gives a short description of
the competition. Section 3makes an overview of both the presented tasks and of the obtained
results. Section 4provides short descriptions of the participating tools. A detailed analysis
of the results for visual expert rounds is presented in Section 5. The results for the textual
expert round are presented in Section 6and the ones for the novice round in Section 7.A
short historical overview over the last rounds of the Video Browser Showdown together with
some advice on developing interactive video search tools are given in Section 8. Section 9
concludes the paper and highlights the most important observations stemming from the
competition.
2 Video browser showdown 2015
VBS 2015 was the fourth iteration of the Video Browser Showdown and took place in
Sydney, Australia, in January 2015, held together with the International Conference on
MultiMedia Modeling 2015 (MMM 2015). Nine teams participated in the competition and
performed ten visual known-item search tasks (Expert Run 1), six textual known-item search
tasks (Expert Run 2), as well as four visual and two textual known-item search tasks with
non-expert users (Novice Run). The shared data set consisted of 153 video files containing
about 100 hours of video content in PAL resolution (720×576@25p) from various BBC
programs, and was a subset of the MediaEval 2013 Search & Hyperlinking data set [15].
The size of the data set was about 32 GB; the videos were stored in webm file format and
encoded with the VP8 video codec, and the Ogg Vorbis audio codec. The data were made
available to the participants about two months before the event.
During the event, users interactively try to solve search tasks with the participating tools;
first in a closed expert session with the developers of a respective tool, then in a public
novice session with volunteers from the audience – typically experts in the field of multime-
dia. Search tasks are so-called known-item search tasks where users search for information
that they are familiar with. For the last two years [41] visual and textual queries were used.
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5542 Multimed Tools Appl (2017) 76:5539–5571
These are clips or textual descriptions of 20-seconds long target segments in a moderately
large shared data set (100 hours at VBS 2015), which are randomly selected on site. After a
clip or the text for a task is presented, participants have to find it in the database. They are
presented through a PC connected to a projector, which runs the VBS Server that presents
target segments (1) through playback of the corresponding clip for visual tasks, and (2)
through presentation of a static textual description of the clip – collaboratively created by the
organizers – for textual queries. After presentation of the visual or textual description, the
VBS Server is responsible for collecting and checking the results found by the participants
and for calculating the achieved score for each team.
The tools of all teams are connected to the VBS Server, and send information about
found segments (frame numbers or frame ranges) to the server via HTTP requests. The
server checks if the segment was found in the correct video and at the correct position
and computes a score for the team, according to a formula that considers the search time
(typically a value between 4 and 8 minutes) and the number of previously submitted wrong
results for the search task (see [2,42]). According to these parameters a team can get up to
100 points for a correct solved task, and in worst case zero points for a wrong or unanswered
task. The scores are summed up and the total score of each session is used to determine
the winner of the session. Finally, the team with the maximum grand total score is selected
as the final winner of the competition. VBS 2015 used three sessions: (1) a visual expert’s
session, (2) a textual expert’s session, and (3) a visual novice’s session.
In order to focus on the interactive aspects of search and avoid focusing too much on
the automatic retrieval aspects, restrictions are imposed. Retrieval tools that only use text
queries without any other interaction feature are therefore not allowed. However, partici-
pants may perform textual filtering of visual concepts, or navigate through a tree of textual
classifications/concepts, for example. Moreover, the Video Browser Showdown wants to
foster simple tools and, therefore, perform a novice session where volunteers from the audi-
ence use the tools of the experts/developers to solve the search tasks and by doing so test
the usability in an implicit way.
In 2015, the focus of the competition has further moved towards dealing with realistically
sized content collections. Thus, the tasks using only single videos, that were present in the
2013 and 2014 editions, have been discontinued, and the data set has been scaled up, from
about 40 hours in 2014 to about 100 hours. The competition started with expert tasks in
which visual and textual queries had to be solved. Then the audience was invited to join
in and the tools were presented to allow the participants to understand how the tools are
used by the experts. In the next sessions members of the audience (“novices”) took over for
visual and text queries, and operated the tools themselves.
Each task in each of the three sessions (visual/textual expert run, novice run) aimed at
finding a 20 seconds query video, where the excerpt does not necessarily start and stop at
shot or scene boundaries. For visual queries, the video clip is played once (with sound) on
a large, shared screen in the room. For textual queries, experts created descriptions of the
contents of the clips, which were displayed on the shared screen and read to the participants.
Participants were given a maximum time limit of eight minutes to find the target sequence
in the corresponding video data (note that in the 2013 and 2014 competitions, the search in
single videos was limited to three minutes, while the archive tasks in the 2014 competition
had a limit of six minutes).
The systems of all participating teams were organized to face the moderator and the
shared screen, which was used for presenting the query videos and the current scores of
all teams via the VBS server. Figure 1shows the setup of the VBS session at MMM2015.
The participating systems were connected to an HTTP-based communication server over a
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Multimed Tools Appl (2017) 76:5539–5571 5543
Fig. 1 Teams competing during the VBS 2015 competition
dedicated Wi-Fi private network. This server computed the performance scores for each tool
and each task accordingly. Each tool provided a submission feature that could be used by
the participant to send the current position in the video (i.e., the frame number or segment)
to the server. The server checked the submitted frame number for correctness and computed
a score for the corresponding tool and task based on the submission time and the number of
false submissions. The following formulas were used to compute the score sk
ifor tool kand
task i,wheremk
iis the number of submissions by tool kfor task iand pk
iis the penalty due
to wrong submissions:
sk
i=
100 50 t
Tmax
pk
i
,(1)
pk
i=1,if mk
i1
mk
i1,otherwise.(2)
The overall score Skfor tool kis simply the sum of the scores of all tasks of the three
sessions. Equations (1)and(2) were designed in order avoid trial-and-error approaches:
participants submitting several wrong results get significantly fewer points than participants
submitting just one correct result. Additionally, the linear decrease of the score over time
should motivate the teams to find the target sequence as fast as possible.
The hardware for the competition was not normalized; all participating teams were free to
use the equipment best supporting the requirements and efficiency of their video browsers.
The teams used notebook computers or tablets, depending on the respective browsing
approaches.
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5544 Multimed Tools Appl (2017) 76:5539–5571
3 VBS2015 evaluation overview
The current section aims to give a general overview of the competition’s tasks and results
and to point towards some of the most interesting conclusions. A detailed analysis and
discussion of the results which focuses on the different tasks types follows in Section 5.
3.1 Overview of the rounds and of presented tasks
As already mentioned in Section 2, the competition focused on two types of tasks, namely
visual and textual tasks.
3.1.1 Expert run 1
In Table 1, an overview of the 10 target clips of the visual expert round (Expert Run 1)is
presented as a series of temporally uniformly sampled frames captured at a two seconds
interval. This should help readers to understand how the presented clips looked like. As
visible in Table 1, some target clips showed quickly changing actions (e.g. tasks 1, 3, 6, 7,
8), only a few tasks - in particular 2 and 10 - showed scenes of longer duration, which are
more distinct but proved hard to find.
3.1.2 Expert run 2
The textual descriptions that the participants were provided with during the competition’s
textual expert round (Expert Run 2) can be read in Table 2.
Tab l e 1 Overview of the presented video targets for the visual experts round
Frame capture at
Task no. 0s 2s 4s 6s 8s 10s 12s 14s 16s 18s
1
2
3
4
5
6
7
8
9
10
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Multimed Tools Appl (2017) 76:5539–5571 5545
Tab l e 2 Descriptions of the target segments provided for the text experts session
Task no. Description
1 Panel of four participants with bluish background on the top (“COMEDIANS” displayed on their
desk below) being asked a quiz question about a Russian exclave (i.e., separated region) in Europe,
after the question is asked close-ups of the people are shown.
2 A man on a meadow (green grass in background), standing next to an ultralight aircraft and getting
into a red and black overall.
3 A group of mostly kids practicing Karate moves indoors (in white clothes), including close-ups of
a blond young woman talking to a girl, and shots showing the instructor, a bald man with glasses.
4 A prairie scenery with a hill on the left and mountains in the background, an old man with a black
suit and hat walking slowly up the hill. He is first seen from behind, then a close-up of the man is
shown. Then a close-up shot of a running wolf in the grass is shown.
5 A red/brown coloured map of Europe, with Alsace and the city of Strasbourg highlighted, showing
also the surrounding countries (e.g., Germany, France). Then black/white shots of soldiers march-
ing in a city (for several seconds). During the whole sequence a female sign language interpreter
is visible in the lower right.
6 A BBC Four trailer, starting with a colourful huge bookshelf, then showing a sequence of
countryside shots, and in each of them a yellow/gold glowing path showing music notes is
appearing.
Tabl e 3shows an overview of the 6 target scenes described by Table 2also as a sequence
of temporally uniformly sampled frames at a two seconds interval. The difficulty with the
textual tasks is the fact that the searchers have no idea about the actual visual presentation
of the scene.
3.1.3 Novice run
As already mentioned, the novice round that followed the visual and textual expert rounds,
consisted of a total of six tasks, out of which four were visual tasks and two were textual
tasks. They were presented as two sequences of two visual tasks followed by a textual task.
Tab l e 3 Overview of the described video targets for the text experts round
Frame capture at
Task no. 0s 2s 4s 6s 8s 10s 12s 14s 16s 18s
1
2
3
4
5
6
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5546 Multimed Tools Appl (2017) 76:5539–5571
Those tasks were extracted from the same pools as the tasks of the previous visual and text
rounds and were in no way different.
The overview of the visual tasks (Task 1, Task 2, Task 4, Task 5) and that of the textual
tasks (Task 3, Task 6) is shown in Table 4, while Table 5gives the descriptions of the two
textual tasks (Task 3 and Task 6 respectively).
3.2 Overview of results
In the following we present the results of the competition rounds. An overview of the final
scores over all these rounds is presented in Fig. 2while the overall submission times for the
successful submissions within the competition across all tasks are shown in Fig. 3.Theaver-
age number of submissions per round and team is shown in Fig. 4. The acronyms in all three
figures’ legends identify the tools of the participating teams: HTW (Germany), IMOTION
(Switzerland-Belgium-Turkey), NII-UIT (Vietnam - Japan), SIRET (Czech Republic), UU
(The Netherlands), VERGE (Greece-UK). Detailed descriptions of those tools are available
in Section 4, while the interested readers might consider the corresponding references in the
Reference list for additional details.
Out of the nine participating teams [3,5,11,22,27,35,37,39,51], six managed to score
points during the competition. Further analysis of the logs showed that one of the three
non-scoring teams managed to solve one of the tasks but submitted its data using a wrong
format.
Some interesting aspects can be observed when looking at both Figs. 2and 3:
The three top ranking teams (SIRET, IMOTION and UU) together with the forth
(HTW) show the most uniform increases in terms of scored points across the visual
expert and novice rounds (Fig. 2). In the case of the text expert round, only the UU and
NII-UIT teams show this pattern. Overall the achievements during the novice round
were over those in the text expert round.
In the case of the NII-UIT team, the best scoring round was that of the novices (it
actually won the round) during which the team climbed up to rank 5.
The slowest of the participants (Fig. 2) was by far the UU team which was almost 2
times slower than the for-last participant in terms of speed - the HTW team. Since the
Tab l e 4 Overview of the presented and described video targets for the novices round
Frame capture at
Task no. 0s 2s 4s 6s 8s 10s 12s 14s 16s 18s
1
2
3
4
5
6
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Multimed Tools Appl (2017) 76:5539–5571 5547
Tab l e 5 Descriptions of the target segments provided for the text novices tasks
Task Description
3 First a close-up of a beehive with many bees, then close-up shots of ants cutting and carrying large
green leaves.
6 Piece about the ESA Ulysses mission, showing an image of the sun and the probe left above it,
while zooming out it is explained how it orbits around the sun.
The next shot shows the sun centered in a greenish hue (“STEREO” image). The flyby of a
rendered model of the probe is shown.
UU team presented a tool designed for human computation this does not come as a
surprise. What comes as a surprise is the excellent score they achieved - rank 3 overall.
Also, it is interesting to note that the UU team was slowest during the visual expert
round and got faster during the text expert and novice rounds with the additional note,
that during the novice round only half of the targets were found (two visual and one
textual targets).
When comparing the three rounds, visual expert, textual expert and novice, the dif-
ference in speed, when it comes to finding the correct target, is not that big as when
comparing experts and novices. The novices are a little bit slower except in the case of
UU team, where the novices actually seem to perform faster then the experts. Unfortu-
nately, due to the small number of novice tasks we are not able to generalize on whether
this has to do with the actual tasks being presented, or this is because the novices just
exploited the tools close to their full potential, as they had no false expectations. Also,
as already mentioned, it is important to note that in most cases, the participants in the
novice round were actually experts from the other participating teams which tested the
“competition’s” tools.
From the scoring point of view we see two team clusters: one that scored over 1000
points (SIRET, IMOTION, UU and HTW) and one that score under 600 (NII-UIT and
VERGE), while from the time point of view, all the teams with the exception of the UU
team, had similar overall completion times for their successful submissions.
We have performed a one-way ANOVA to determine if the successful submission
times for the visual experts round was different for the participating teams. Each of
the IMOTION, SIRET and UU teams had one outlier. The search time was normally
distributed for all interfaces, as assessed by Shapiro-Wilk’s test (p >.05). There was
Fig. 2 Total score of teams in the VBS 2015 competition
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5548 Multimed Tools Appl (2017) 76:5539–5571
Fig. 3 Box plot of the submission time per team in the VBS 2015 competition, based on correct submissions
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Multimed Tools Appl (2017) 76:5539–5571 5549
Fig. 4 Average number of submissions (correct and wrong) in the VBS 2015 competition
homogeneity of variances, as assessed by Levene’s test for equality of variances (p =
.92). The search time was statistically significantly different between the interfaces,
F(5, 30) = 3.045, p <.05.
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5550 Multimed Tools Appl (2017) 76:5539–5571
4 Scoring video search tools in VBS2015
4.1 IMOTION
The IMOTION system [39] is a sketch and example-based video retrieval system. It is based
on a content-based video retrieval engine called Cineast [38] that focuses on Query-by-
Sketch and also supports Query-by-Example and motion queries.
In IMOTION, a user can specify a query by means of a sketch that may include edge
information, color information, motion information, or any combination of these, or provide
sample images or sample video snippets as query input. It uses multiple low-level features
such as color- and edge histograms for retrieval.
The IMOTION system extends the set of features by high level visual features such as
state-of-the-art convolutional neural network object detectors and motion descriptors. All
feature vectors along with meta-data are stored in the database and information retrieval
system ADAM [16] which is built upon PostgreSQL and is capable of performing efficient
vector space retrieval together with Boolean retrieval.
The browser-based user interface, which is optimized to be usable with touch screen
devices, pen tablets as well as a mouse, provides a sketching canvas as well as thumbnail
previews of the retrieved results.
Figure 5shows an example query with corresponding results. The results are grouped by
row, each row containing shots which are similar to the query by a different measure such
as colors, edges, motion or semantics. The topmost row shows the combination of these
individual result lists whereas the influence of each category can be adjusted by sliders
which change the combination in real time. The UI also offers a video capture functionality
to collect reference frames using a webcam which then could be used during retrieval. Video
capturing was successfully used during the visual tasks where images from the webcam
were used as queries directly after cropping. In certain cases, the images were modified
using the sketching functionality. For the textual challenges, only sketches were used.
4.2 SIRET
The SIRET system [5] is a new version of the Signature-based Video Browser tool (SBVB)
[33] that was successfully introduced at the Video Browser Showdown in 2014. The
Fig. 5 Screenshot of the IMOTION system
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Multimed Tools Appl (2017) 76:5539–5571 5551
tool combines two orthogonal approaches – efficient filtering using simple color-based
sketches and enhanced presentation/browsing of the results. Both the filtering and the
browsing parts of the application received various adjustments. Nonetheless, the over-
all concept utilizing position-color feature signatures [30,40] was preserved, because
representation of key-frames by the feature signatures enables effective and efficient loca-
tion of searched key-frames. The concept relies on the Query-by-Sketch approach, where
simple sketches representing memorized color stimuli can be quickly defined by posi-
tioning colored circles (see the right side of Fig. 6). The tool enables users to define
either one sketch or two time-ordered sketches. In case when two sketches are specified,
the tool searches for clips having matching key-frames in this particular order. The two
searched key-frames have to be within a user specified time window. The retrieval model
was described in more detail in [6]. The current enhanced version of the tool also con-
siders the complexity of the key-frames to automatically adjust settings of the retrieval
model.
Every query sketch adjustment is projected to the results area (see the left side of Fig. 6)
immediately thanks to the efficient retrieval model employed which is based on position-
color feature signatures. Each row represents one matched scene delimited by the matched
key-frames (marked with red margin) and accompanied by a few preceding and following
key-frames from the video clip. Any displayed scene can be selected as either positive or
negative example for additional filtering. Alternatively, particular colored circles might be
picked from displayed key-frames to the sketches similarly to picking up a color with the
eyedropper tool. Regarding the video-level exploration, users may exclude a particular video
from the search or contrary, focus on a single video. Especially the later mentioned feature
often led to success as its appropriate usage can significantly increase results relevancy.
When exploring a single video, users may find useful the extended results row (see the
bottom of Fig. 6) enriched with Interactive Navigation Summary [43] displaying (in this
case, 5 dominant colors of each key-frame).
Fig. 6 Screenshot of the SBVB tool in action
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5552 Multimed Tools Appl (2017) 76:5539–5571
4.3 HTW
The HTW system [3] is a map-based browsing system for visually searching video clips
in large collections. Based on ImageMap [4], it allows the user to navigate through a
hierarchical-pyramid structure in which related scenes are arranged close to each other. An
extended version of ImageMap can be viewed online at www.picsbuffet.com. The inter-
action is similar to map services like Google Maps: a view port revealing only a small
portion of the entire map at a specific level. Zooming in (or out) shows more (or less)
similar scenes from lower (or higher) levels. Dragging the view shows related images
from the same level. While the hierarchical-pyramid of all scenes in the data set (“Map of
Scenes”) has been precomputed to avoid performance issues, the map for a single video
is generated on the fly and can therefore be filtered or altered based on the actions of the
user.
The HTW-Berlin video browsing interface is divided into three parts: the brows-
ing area on the left, the search result area in the middle and the search input area on
the right.
Generally the user starts with a sketch and maybe some adjustments to the bright-
ness/contrast and saturation of the input. In the meanwhile the tool updates all views in
real-time and presents the best match as a paused video frame on the bottom right of the
interface (Fig. 7). The “Map of Scenes” jumps to a position where the frame of the video is
located and other similar looking scenes are displayed in the result tab. If the detected scene
is not the right one, the user can use the ImageMap to find related scenes and start a new
search query by clicking them. All views are updated again and the sketch gets replaced by
the selected frame. Upon finding the right scene it is suggested to check the “Video Map” for
multiple look-alike alternatives and use the “Video Sequence” to verify the correct adjacent
key frames.
Fig. 7 Screenshot of the HTW-Berlin tool
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Multimed Tools Appl (2017) 76:5539–5571 5553
In case the content of a scene is described as text or verbally, less or no visual information
may be available, making a search nearly unfeasible. Usually the user still has an idea how
the scene might look like. With the help of ImageMap it is possible to quickly navigate and
check potential key frames.
4.4 UU
The UU system [27] excludes all kinds of video analysis and machine-based query process-
ing and relies exclusively on interaction design and human browsing abilities. Past research
has demonstrated that a good and efficient interaction design can have a significant impact
on search performance [24,49] – especially when searching in single video files or small
data sets. This claim is supported by previous years’ VBS results, for example, the baseline
study presented in [42].
Assuming that a simplistic design will increase search performance, all data is presented
in a storyboard layout, i.e., a temporarily sorted arrangement of thumbnail images repre-
senting frames extracted from the videos. Considering that no video analysis is applied,
these thumbnails have to be extracted at a low step size. Here, one second is used, resulting
in about 360,000 single thumbnails for the approximately 100 hours of video. It is obvious
that browsing such a huge amount of images in a short time is only possible if the related
system is optimized for speed and the search task at hand. Figure 8illustrates the related
design decisions. Targeting a tablet as device with 9 inch screen size, and based on previ-
ous research about optimal images sizes for storyboards on mobiles [25,26], 625 images
are represented on one screen (cf. Fig. 8a). In order to better identify scenes, thumbnails
are arranged not in the common left/right-then-top/down order but a mixture of up/down-
left/right directions (cf. Fig. 8b). With 625 thumbnails on one screen and a total amount
of about 360,000 thumbnails, more than 550 screens have to be visually inspected if the
whole database has to be browsed. In order to speed up this process and considering related
Fig. 8 UU’s interface for purely human-based video browsing
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5554 Multimed Tools Appl (2017) 76:5539–5571
research results [23], interaction is simplified and restricted to up/down motions (i.e., sto-
ryboards of all files are represented horizontally; cf. Fig. 8c) and navigation is limited to
discrete jumps between single screens or video files (cf. Fig. 8d).
4.5 VERGE
The VERGE system [35] is an interactive retrieval system that combines advanced retrieval
functionalities with a user-friendly interface, and supports the submission of queries and the
accumulation of relevant retrieval results. The following indexing and retrieval modules are
integrated in the developed search application: a) Visual Similarity Search Module based
on K-Nearest Neighbour search operating on an index of lower-dimensional PCA-projected
VLAD vectors [28]; b) High Level Concept Detection for predefined concepts by training
Support vector machines with annotated data and five local descriptors (e.g. SIFT, RGB-
SIFT, SURF, ORB etc), which are compacted and aggregated using PCA and encoding; the
output of the trained models is combined by means of late fusion (averaging); c) Hierar-
chical Clustering incorporating a generalized agglomerative hierarchical clustering process
[29], which provides a structured hierarchical view of the video keyframes.
The aforementioned modules allow the user to search through a collection of images
and/or video keyframes. However, in the case of a video collection, it is essential that the
videos are preprocessed in order to be indexed in smaller segments and semantic informa-
tion should be extracted. The modules that are applied for segmenting videos are: a) Shot
Segmentation; and b) Scene Segmentation. All the modules are incorporated into a friendly
user interface (Fig. 9) in order to aid the user to interact with the system, discover and
retrieve the desired video clip.
Fig. 9 VERGE video retrieval engine interface
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Multimed Tools Appl (2017) 76:5539–5571 5555
5 Evaluation details on visual experts tasks
5.1 Search time and achieved points
The breakdown of the final scores per task as well as the information regarding the time
needed for the completion of each successful submission are presented in Figs. 10 and 11.
The SIRET team completed 8 out of 10 proposed tasks, while the IMOTION team suc-
cessfully completed 9 of the 10 tasks. The UU and HTW teams completed 7 tasks while the
NII-UIT and VERGE teams completed 2 tasks each.
From Table 6we can see that these two teams (in particular NII-UIT) submitted many
wrong results, which however were quite visually similar to the targets. The frames for all
the false submissions are shown in Table 6as thumbnails with red contour. It can be seen
that, most of the times, the visual similarity when compared with the target scenes is very
high (see both Tables 1and 7- the thumbnails with the green contour). This is because the
majority of the tools concentrated on the visual features, which in cases of similar/identical
looking frames from different segments/shots does not suffice for correctly identifying the
target scene. In those cases, additional information like for example the audio track, is
needed.
A closer examination of Fig. 11 hints towards some interesting aspects:
The IMOTION team had a slow start during the first 2 tasks, but then submitted the
correct target scene very quick for the next 7 tasks. In fact they were the quickest for
task number 3, 5, 7, 8 and 9.
The same slow start during the first tasks can be seen in the case of the other teams:
SIRET, HTW and UU. This might be due to an accommodation phase in which the
teams got accustomed to the competition spirit as well as with the responsiveness of the
various tools’ features under the on-site conditions.
In the case of the three teams that successfully completed the first two tasks IMO-
TION, SIRET and HTW, the time needed to complete the tasks actually increases:
this can be explained either by the fact that the target scene is located “deep” within
the archive and more time is needed for investigation, or by the fact that they tried
to apply for the second round the same strategy they employed for the first one and
failed.
Fig. 10 Breakdown per individual tasks of the scores for the visual experts round
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5556 Multimed Tools Appl (2017) 76:5539–5571
Fig. 11 Breakdown per individual tasks of the time needed for the successful submissions for the visual
experts round
For the UU team which had a tool that relied heavily on human computation, the time
needed to successfully find a target scene shows the lowest variance with the exception
of task 4 in which actually the UU team was the fastest (this might be due to the posi-
tioning of the target scene at the very beginning of the video and the navigation model
employed).
The tasks 3, 4 and 6 were successfully completed by 5 teams; in the case of tasks 3
and 4 by the same teams configuration: IMOTION, SIRET, UU, HTW and NII-UIT.
The tasks 5, 9 and 10 were completed by 4 teams, tasks 1 and 2 by 3 teams while
tasks 7 and 8 were completed by only 2 teams: IMOTION and UU. While in the case
of IMOTION it seems that the internals have played the most important role, since the
team was fastest for exactly those two “difficult” tasks, in the case of UU it seems
to be raw human power that had been rewarded - in the case of task 7 the UU team
had the slowest completion time over all tasks (not in comparison with the other teams
though).
5.2 Erroneous Submissions
It is interesting to note that the IMOTION and UU teams always identified the correct
files, while the HTW and SIRET teams each had 1, and 2 wrongly identified files respec-
tively, but in all 3 cases the correct file was later identified. The UU team achieved the best
ratio for correct submissions vs. wrong submissions with 8 correct submissions to 3 wrong
submissions.
The distances in terms of frame numbers from the submitted segment center to the tar-
get segment center for both right and false submissions are presented in Fig. 12 (Fig. 12a
for right/successful submissions and Fig. 12b for false submissions within the correct file).
Negative values in both sub-figures, represent submissions in the first half of the target seg-
ment or frames leading up to the target segment, while positive values represent submissions
in the second half of the target segment or frames past the target segment up to the end of
the video.
In the case of the successful submissions (see Fig. 12a) a greater number of submissions
have been issued with frames from the second half of the target segment (positive values),
although there is also a significant number of submissions from the first half. In the case of
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Multimed Tools Appl (2017) 76:5539–5571 5557
Tab l e 6 Frames submitted during the visual experts round (wrong submissions have red contours; right
submissions have green contours)
Team Task
12345678910
SIRET
IMOTION
UU
HTW
NII-UIT
VERGE
...
the false submissions (Fig. 12b) most of them are made by indicating frames/segments past
the target segment and later in the video.
The actual frames sent for validation by the participating teams, for all the successful
submissions, can be seen in Table 6as thumbnails with green contour (in the case of the
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5558 Multimed Tools Appl (2017) 76:5539–5571
Tab l e 7 Frames submitted by the participant teams during the textual experts round (wrong submissions
have red contours; right submissions have green contours)
Team Task
1 234 56
SIRET
IMOTION
UU
HTW
NII-UIT
VERGE
IMOTION team which sent a frame range, as permitted by the competition rules, we have
chosen the central frame of the sent segment).
6 Evaluation details on textual experts tasks
The final scores at the end of the text round are also shown in Fig. 2. This proved to be
the most challenging round of the competition. From the nine participating teams, only the
UU team managed to score more than 50 % of the possible points for the session, with 367
points out of 600, while VERGE and SIRET scored close to 33 % with 188 and 166 points
respectively. The performance of the UU team is particularly surprising since it employed
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Multimed Tools Appl (2017) 76:5539–5571 5559
only human computation and only static small thumbnails (no audio or video playback
capabilities). Those results show there is still enough room for improvement in this area.
Figures 13 and 14 show the breakdown of the final scores per task as well as the infor-
mation regarding the time needed for the completion of each successful submission for the
tasks in the text experts’ round.
Regarding task completion, no team managed to solve Task 4, while Task 1 and Task 6
were solved only by the UU team. Task 2 and Task 3 were successfully solved by 3 teams,
while Task 5 seams to have been the easiest, with 5 teams solving it. All teams scored over
50 % of the available points per task (more than 50 points) and as in the case of the visual
round, no successful submission was made past the 5 minutes mark.
7 Evaluation details on the novice tasks
Figure 2also shows the scores obtained by each of the participating teams for the visual
and text tasks of the novice round. With four visual tasks and two text tasks, the maximum
possible scores were 400 for the visual and 200 for the text tasks. This gave an overall of
600 possible points for the novice round, as much as the text expert round.
Fig. 12 Breakdown per tasks for teams’ submissions distances from target segment center for visual experts
session
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5560 Multimed Tools Appl (2017) 76:5539–5571
Fig. 13 Breakdown per individual tasks of the scores for the text experts round
IMOTION obtained the highest score for the visual novice (340 points, close to the
maximum of 400), but the lowest score (28 points from the maximum of 200) for the text
novice. NII-UIT, HTW, SIRET, VERGE and UU also scored high in the visual novice tasks.
For the text novice tasks, NII-UIT, HTW and SIRET obtained the highest scores, with
NII-UIT scoring a surprisingly high score of 181 points. The UU team also managed to
score 90 points while, as already mentioned, IMOTION were last in this category with only
28 points. VERGE scored no points for the text tasks in the novice round, which is very
surprising, since their concept-based search tool seems to be particularly well suited for
novices.
The breakdown per tasks of the scores for the novice round as well as the time needed
for the correct submissions are shown in Figs. 15,and16 respectively. The scores obtained
for all 4 visual tasks (Task 1, Task 2, Task 4, Task 5) were high and very high for all the
novices - all scored over 60 points. This was true also for the 2 text tasks (Task 3 and Task
6), with the 2 notable exceptions of the IMOTION and SIRET teams in the case of Task 6
for which both achieved under 50 points. When looking at the time needed for submitting a
correct answer as shown in Fig. 16, it can be seen that it was way under half of the maximal
available time in most of the cases.
Fig. 14 Breakdown per individual tasks of the time needed for the successful submissions for the text experts
round
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Multimed Tools Appl (2017) 76:5539–5571 5561
Fig. 15 Breakdown per individual tasks of the scores for the novices round for both visual and textual tasks
Overall, as also seen from Fig. 4c which presents the average number of submissions
(wrong as well as correct) per team for the novice round, the participants seemed more than
cautious when submitting frames for validation. In fact the novice round had overall the
smallest number of wrong submissions when compared with both expert rounds. We have
two possible non-exclusive explanations for this:
it was the final round which was to decide the winner in a very tight competition and
the participants were over-cautious;
the majority of the“novices” were in fact members of the participating teams testing the
“competition’s” tools under their colleagues close supervision and they did not want to
“sabotage” their winning chances by making wrong submissions and by this achieving
a low score. At that point we want to mention that the novice session in general is kind
of problematic for the final analysis, as it may distort the results. Therefore, we might
want to skip it in future iterations of the VBS.
A closer inspection revealed that there is no difference between the two types of tasks
(visual vs. text) from the outcome of submissions point of view. It can be seen though, that
Task 6 seemed more difficult since it had overall the largest number of false submissions
in both the right and wrong files. It has also been a textual task. The unusual large number
Fig. 16 Breakdown per individual tasks of the time needed for the successful submissions for novice round
for both visual and textual tasks
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5562 Multimed Tools Appl (2017) 76:5539–5571
of submissions for Task 6 when compared to the other five previous tasks could be also
explained by the fact that it was the last task and by an all-or-nothing approach from some
of the participants. In fact, the winner of the competition was decided by novices during this
last task, when SIRET overcame IMOTION, after three false submissions for each team:
two vs. one from a wrong file for SIRET when compared to IMOTION. The difference was
made by the speed of the submissions, with SIRET being twice as fast as IMOTION for this
particular task with 117049 ms vs. 268769 ms.
8 Development of interactive video search tools
VBS sessions happen to be indeed interactive and VBS 2015 was not an exception. Par-
ticipants are exploring tools of other teams and the audience often discuss the approaches
during breaks etc. It is thus natural to adapt and perhaps enhance a well-performing fea-
ture introduced by some other participant. Thanks to the gradual improvements, a tool
winning the competition one year would probably fail without further development the
next year. Several teams participated steadily for the last few years, each year improv-
ing their tools, adding modalities, features or ever reworking their concepts from scratch.
We may ask then, are there any trends that we can distinguish? Can we find a com-
mon feature that is sooner or later incorporated by almost every team? And lastly,
can we derive guides or best practices for developing such interactive video search
tool?
In the following paragraphs, we track the evolution of the tools which had won VBS in
one of the previous years, namely teams AAU (2012), NII-UIT (2013) and SIRET (2014
and 2015).
NII-UIT established themselves in 2013 by actually winning VBS [31]. The tool utilized
filtering by prior-detected concepts and visual content, a grid of dominant color more pre-
cisely. The results were presented with a coarse-to-fine hierarchical refinement approach.
In 2014 they came up with quite a similar tool with one important enhancement – a user
could define a sequence of patterns, i.e., define two sets of filters and search for clips having
two matching scenes in the same order [36]. Finally, in 2015 they additionally focused on
face and upper body concepts together with audio filters and replaced the grid of dominant
colors with less rigid free-drawing canvas [37].
The tool which was introduced by SIRET team in 2014 [33] appeared quite different.
Instead of complex processing pipelines, such as state-of-the-art concept detectors etc., the
authors employed only one feature capturing color distribution of key-frames (so called
feature signatures) together with convenient sketch drawings (apparently, NII-UIT adapted
sketch drawings later on). Surprisingly it was enough to win the VBS that year. Note
that similarly to NII-UIT’s tool, users were allowed to specify two consecutive sketches
to improve the filtering power which seemed to be quite effective. Changes introduced to
the tool in 2015 [5] were rather subtle, focusing the browsing part of the tool, such as
compacting static scenes in order to save space etc.
The list of winners of previous VBS competitions is completed by AAU tool from
2012 [14] which is somewhat similar to the most recent UU’s application, both exhibit-
ing surprisingly powerful human computation potential. In this case, the videos are simply
scanned in parallel during the search time without any prior content analysis.
Overlooking all the various approaches, we can identify three main techniques appearing
repetitively:
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Multimed Tools Appl (2017) 76:5539–5571 5563
Content-based filtering may be based on either high-level concepts or low-level fea-
tures. In particular, most of the participants used some kind of color-based filtering
and although their filtering power decreases steadily as the dataset size increases, they
seem to remain quite effective. Also, temporal filtering (e.g., two content sketches that
focus two neighboring segments in a video) seem to be quite effective, because static
approaches (focusing on a single image only) do not work well with the sheer amount
of frames.
Browsing is perhaps a crucial part that cannot be avoided. In many cases, the number
of relevant results is simply too large to fit one screen and users need an effective and
convenient way to browse through the results.
As users do so, they will probably encounter scenes quite similar to the searched one
which may be used as a query for additional similarity search. By giving the search
engine either positive or negative examples users can rapidly navigate themselves
towards the target if an appropriate similarity model is employed. Note that regarding
the textual tasks, we face the problem of proper initialization of this similarity search
loop.
At this moment, we do not see a single approach, feature or concept that is clearly out-
performing the others. We believe, though, that a successful interactive video search tool
has to incorporate all the three techniques mentioned above.
It is of course hard to predict the future in this challenging field. However, we can assume
that future systems will also strongly rely on color-based filtering (e.g., color maps such
as used by the system described in Section 4.3), on concept-based filtering (e.g., visual
semantic concepts detected with deep learning approaches), on temporal filtering as well
as on improved content visualization with several techniques, for example with hierarchical
refinement of similar results. Since the VBS plans to increase the size of the data set every
year, we believe that in the long run the biggest challenge will be the efficient handling of
the large amount of content, i.e., content descriptors and indexes, and providing a highly
responsive interactive system that allows for iterative refinement.
9 Conclusions
In the context of the discussion that follows, we would like to highlight the fact that all
target segments for the three rounds, were randomly generated from the 154 files totaling
over 100 hour of video material that formed the competition dataset. Approximately 10%
of the cues had also textual description assigned by the organizers. From within those two
pools, the target videos for the competition rounds were randomly chosen: ten targets for
the visual expert round, six targets for the text expert round and six targets for the novice
round (four targets for visual tasks and two targets for text tasks).
The case of the novice round differs a little bit from the two expert rounds, because the
visual and textual tasks were mixed and not consecutive. Also, because of time constraints,
the organizers were able to allow only six novice tasks out of which four were visual (Task
1, Task 2, Task 4 and Task 5) and two were textual tasks (Task 3 and Task 6).
Some interesting facts emerge when looking and comparing the figures presenting the
breakdowns per individual tasks of the scores and of the times needed for the correct
submissions for the three rounds:
The visual tasks in the novice round achieved the best overall scores across all teams
when compared with the visual tasks in the visual expert round.
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5564 Multimed Tools Appl (2017) 76:5539–5571
the text tasks in the novice round (2 tasks) achieved comparable results with the best
performances across the 6 tasks in the text expert round.
The completion time in the case of the visual and text tasks in the novice round is
comparable with the completion time in the case of the visual and text expert rounds.
The main difference is that the advantage that the SIRET, IMOTION and some-
times UU teams had in the expert rounds, is much reduced in the case of the novice
round.
The novice round brought the best performance for the NII-UIT team in both scored
points and speed. Actually for the first text task in the novice round, the NII-UIT team
achieved the best score and had the fastest correct submission.
It is also interesting to have a closer look at the frames being submitted across the three
competition rounds, both the ones of the correct submissions as well as the ones of the
wrong submissions (both from the correct and wrong files) and to compare them with the
uniform sampled frames of the video targets. The tables in question are Table 1,Table1and
Tabl e 4for the overview of the target videos and Tables 6and 7for the correct submitted
frames as well as for the wrong submitted frames. Some interesting observations can be
made:
Within each of the scenes used as targets in the visual expert round there are multiple
highly similar images (this is also apparent in Table 1as well as Tables 3and 4which
display overviews of the 20 seconds long target scenes while using 2 second granularity
for each image). Because of the granularity used in the figures, not all the details are
visible, from here the difference in terms of actually submitted frames.
The scenes are very diverse including indoor and outdoor shots as well as overlays of
computer generated content spread across TV reporting, TV series, TV documentaries.
The best results were obtained by tools that employed some form of sketching for an
query-by-example approach, as in the case of the SIRET and IMOTION teams, or that made
heavy use of browsing, like in the case of the UU team which had an approach centered
on human computation. All those tools had effectively put the user in the center of their
approaches to an interactive multimedia retrieval system and had tried to exploit its mental
and physical capacities to their fullest in order to solve the proposed tasks. The results of
the text tasks during both the expert and novice rounds show that there is still a lot of room
for improvement and that in this particular case further research is needed.
Acknowledgments Open access funding provided by University of Klagenfurt.
The authors would like to thank all the colleagues that participated in the development of the VBS2015
tools, the colleagues that took part in the VBS2015 competition as well as the colleagues that took time
to read and make observations on draft versions of this paper: Laszlo B¨
osz¨
ormenyi, Marco A. Hudelist,
Anastasia Moumtzidou , Vasileios Mezaris, Ioannis Kompatsiaris, Rob van de Werken, Nico Hezel, Radek
Mackowiak, Ivan Giangreco, Claudiu T˘
anase, Heiko Schuldt.
The video content used for VBS is programme material under the copyright of the British Broadcasting
Corporation (BBC), kindly provided for research use in the context of VBS.
This work was funded by the Federal Ministry for Transport, Innovation and Technology (bmvit) and Aus-
trian Science Fund (FWF): TRP 273-N15 and the European Regional Development Fund and the Carinthian
Economic Promotion Fund (KWF), supported by Lakeside Labs GmbH, Klagenfurt, Austria.
The research leading to these results has received funding from the European Union’s Seventh Frame-
work Programme (FP7/2007-2013) under grant agreement no. 610370, ICoSOLE (“Immersive Coverage of
Spatially Outspread Live Events”, http://www.icosole.eu).
This research has been supported by Charles University Grant Agency project 1134316.
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Multimed Tools Appl (2017) 76:5539–5571 5565
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Inter-
national License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if changes were made.
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Claudiu Cobarzan received his PhD. degree from Babes-Bolyai University, Romania in August 2009 under
the joint supervision of Prof. Dr. Florian Mircea Boian and O.Univ.-Prof. Dipl.-Ing. Dr. Laszlo Boeszoer-
menyi. The Ph.D. thesis concentrated on distributed video proxy-caching in highbandwidth networks. From
2006 to 2009 he worked as a Teaching Assistant at Babes-Bolyai University and from 2009 to 2013 he was
a Lecturer Professor at the same university. From 2013 to 2016 he held a Post Doc position at Klagenfurt
University within the Next Generation Video Browsing project.
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5568 Multimed Tools Appl (2017) 76:5539–5571
Klaus Schoeffmann is an associate professor in the distributed multimedia systems research group at the
Institute of Information Technology (ITEC) at Klagenfurt University, Austria. He received his Ph.D. in 2009
and his Habilitation (venia docendi) in 2015, both in Computer Science and from Klagenfurt University. His
research focuses on human-computer-interaction with multimedia data (e.g., exploratory video search), mul-
timedia content analysis, and multimedia systems, particularly in the domain of medical endoscopic video.
He has co-authored more than 70 publications on various topics in multimedia and he has co-organized inter-
national conferences, special sessions and workshops (e.g., MMM 2012, CBMI 2013, VisHMC 2014, MMC
2014 - MMC 2016). He is co-founder of the Video Browser Showdown (VBS), an editorial board member
of the Springer International Journal on Multimedia Tools and Applications (MTAP), Springer International
Journal on Multimedia Systems, and a steering committee member of the International Conference on Mul-
tiMedia Modelling (MMM). Additionally, he is member of the IEEE and the ACM and a regular reviewer
for international conferences and journals in the field of multimedia.
Werner Bailer studied Media Technology and Design at the University of Applied Sciences in Hagenberg
(Upper Austria). He graduated in 2002 with a diploma thesis on “Motion Estimation and Segmentation for
Film/Video Standards Conversion and Restoration”. This work was performed at the Institute of Informa-
tion Systems and Information Management at JOANNEUM RESEARCH, where he works since 2001 as
a research engineer. His main research interests are algorithms for video content analysis and digital film
restoration, metadata description of audiovisual content (with a focus on MPEG-7) and system architectures
of media processing systems. He is author of the tutorial “Writing ImageJ Plugins”.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Multimed Tools Appl (2017) 76:5539–5571 5569
Wolfgang H¨
urst is an assistant professor at the Department of Information and Computing Sciences and a
lecturer in the bachelor program “Gametechnologie” and the master program “Game and Media Technology”
at Utrecht University, The Netherlands. His research interests include mobile computing, human-computer
interaction, computer graphics, and multimedia systems and technologies, mostly with a focus on gaming
and media interaction. Hrst has a PhD in computer science from the University of Freiburg, Germany, and a
master in computer science from the University of Karlsruhe (TH), Germany. From January 1996 till March
1997 he was a visiting researcher at the Language Technologies Institute at Carnegie Mellon University in
Pittsburgh, PA, USA. From March 2005 till October 2007 he worked as a teaching and research associate at
the Faculty for Applied Sciences at the University of Freiburg, Germany. He is a member of IEEE Computer
Society, ACM, ACM SIGMM, ACM SIGGRAPH, ACM EuroMM, and GI (Germany).
Adam Blaˇ
zek graduated with honors from Computer Science at Charles University in Prague in 2014. His
research topics are similarity search, video retrieval, and multimedia indexing. He introduces himself to the
field of video search and browsing by winning the annual competition Video Browser Showdown 2014.
Besides his studies, he also works as a researcher and project manager at IBM.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
5570 Multimed Tools Appl (2017) 76:5539–5571
Jakub Lokoˇ
creceived the doctoral degree in software systems from the Charles University in Prague, Czech
Republic. He is an assistant professor in the Department of Software Engineering at the Charles University
in Prague, Faculty of Mathematics and Physics, Czech Republic. He is a member of siret research group
and his research interests include metric access methods, multimedia retrieval and exploration, and similarity
modeling.
Stefanos Vrochidis received the Diploma degree in Electrical Engineering from Aristotle University of
Thessaloniki, Greece, the MSc degree in Radio Frequency Communication Systems from University of
Southampton and the PhD degree in Electronic Engineering from Queen Mary University of London. Cur-
rently, he is a Postdoctoral Researcher with the Information Technologies Institute. His research interests
include semantic multimedia analysis, indexing and information retrieval, data mining, search engines and
human interactions, as well as digital TV learning and environmental applications. Currently Dr. Vrochidis
is the Scientific Manager and the deputy Project Coordinator of the FP7 project MULTISENSOR and the
Interaction Coordinator of the Cost Action iV&L: The European Network on Integrating Vision and Lan-
guage (iV&L Net): Combining Computer Vision and Language Processing For Advanced Search, Retrieval,
Annotation and Description of Visual Data. Dr. Vrochidis has successfully participated in many European
and National projects and he has been involved as a co-author in more than 60 related scientific journal,
conference and book chapter publications.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Multimed Tools Appl (2017) 76:5539–5571 5571
Prof. Dr.-Ing. Kai-Uwe Barthel studied Elektrotehnik at the Technical University in Berlin. Till his gradua-
tion in 1996 he worked as an Assistant at the Institute for Telecommunication and Theoretical Elektrotehnik.
He was involved in the standardization process for the JPEG2000 while working at NTEC Media GmbH
Berlin and LuraTech GmbhH Berlin. Since 2001 he is a Professor at HTW Berlin. His main research inter-
ests are Information Retrieval, Machine Learning, Visualisation, Computer Vision and visual clustering &
sorting. He is the founder (2009) of the pixolution GmbH.
Luca Rossetto received his M.Sc. in Computer Science in 2014 from the University of Basel in Switzerland
where he is currently pursuing a PhD in the area of Information Retrieval with special focus on Query
Processing methods for Content-based Video Retrieval.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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Chapter
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This paper introduces a video browsing tool for the known item search task. The key idea is to reduce the number of segments to further investigate by several ways such as applying visual filters and skimming representative keyframes. The user interface is optimally designed so as to reduce unnecessary navigations. Furthermore, a coarse-to-fine based approach is employed to quickly find the target clip.
Conference Paper
In this paper, an interactive system for human action video search is developed based on the dynamic shape volumes. The user is allowed to create a search query by freely and continuously posing any number of actions in front of the Kinect sensor. For the captured query video sequence and each data stream of the human action video database, we extracted useful shape properties on the basis of space-time volumes by exploiting the solution to the Poisson equation. Different from conventional learning-based human action recognition techniques, we apply approximate string matching (ASM) to achieve local alignment for the matching of two video sequences. The experiments demonstrate the effectiveness of our system in support of the user’s search task.
Conference Paper
This paper presents an interactive tool for searching a known item in a video or a video archive. To rapidly select the relevant segment, we use query patterns formulated by users for filtering. The patterns can be formulated by drawing color sketches or selecting predefined concepts. Especially, our tool support users to define patterns for sequences of consecutive segments, for instance, sequences of occurrences of concepts. Such patterns are called sequential patterns, which are more powerful to describe users' search intention. Besides that, the user interface is organized following a coarse-to-fine manner, so that users can quickly scan the set of candidate segments. By using color-based and concept-based filters, our tool can deal with both visual and descriptive known item search.
Article
We describe Dublin City University (DCU)'s participation in the Hyperlinking sub-task of the Search and Hyperlinking of Television Content task at MediaEval 2013. Two methods of video hyperlinking construction are reported: i) using spoken data annotation results to achieve the ranked hyperlink list, ii) linking and merging meaningful named entities in video segments to create hyperlinks. The details of algorithm design and evaluation are presented.