Nozha Boujemaa

Nozha Boujemaa
National Institute for Research in Computer Science and Control | INRIA

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200
Publications
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3,839
Citations

Publications

Publications (200)
Article
Pl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requir...
Book
Pl@ntNet est un réseau humain s'appuyant sur une infrastructure informatique, permettant l'identification, l'agrégation et le partage d'observations botaniques à très grande échelle. Cette initiative mobilise différentes institutions de recherche dans divers champs scientifiques (informatique, agronomie, écologie) et de larges réseaux associatifs d...
Conference Paper
We present two complementary botanical-inspired leaf shape representation models for the classification of simple leaf species (leaves with one compact blade). The first representation is based on some linear measurements that characterise variations of the overall shape, while the second consists of semantic part-based segment models. These repres...
Article
Full-text available
This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of...
Article
Structuring the search space based on domain-specific vocabulary (or concepts) is capital for enhanced image retrieval. In this paper, we study the opportunities and the impact of exploiting such a strategy in a particular problem which is the leaf species identification. We believe that such a solution is promising to reduce the effect of the high...
Article
Full-text available
Pl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requir...
Conference Paper
Full-text available
This paper presents several improvements of Pl@ntNet1, an image sharing and retrieval application for identifying plants [6]: (i) ported to most android platforms (ii) three times more data (iii) exploiting metadata as well as visual content in the identification process (iv) a new multi-plant-organ, multi-image and multi-feature merging strategy w...
Conference Paper
Full-text available
Leaves of plants can be classified as being either simple or compound according to their shapes. Compound leaves can be seen as a collection of simple leaf-like structures called leaflets. However, most computer vision-based approaches describe these two leaf categories similarly. In this paper, we propose a new description and identification metho...
Article
Full-text available
Shonan meeting on "The Future of Multimedia Analysis and Mining," was organized from 3 to 6, November, 2012. This technical report summarizes the program of the meeting for record.
Article
We present a new hierarchical strategy for fine-grained categorization. Standard, fully automated systems report a single estimate of the category, or perhaps a ranked list, but have non-neglible error rates for most realistic scenarios, which limits their utility. Instead, we propose a semi-automated system which outputs a it confidence set (CS)—a...
Article
Full-text available
State-of-the-art visual search systems allow to retrieve efficiently small rigid objects in very large datasets. They are usually based on the query-by-window paradigm: a user selects any image region containing an object of interest and the system returns a ranked list of images that are likely to contain other instances of the query object. User’...
Article
Full-text available
This paper describes the participation of Inria within the Pl@ntNet project7 at the LifeCLEF2014 plant identication task. The aim of the task was to produce a list of relevant species for each plant observation in a test dataset according to a training dataset. Each plant observation contains several annotated pictures with organ/view tags: Flower,...
Book
This paper presents several improvements of Pl@ntNet1, an image sharing and retrieval application for identifying plants [6]: (i) ported to most android platforms (ii) three times more data (iii) exploiting metadata as well as visual content in the identification process (iv) a new multi-plant-organ, multi-image and multi-feature merging strategy w...
Conference Paper
Full-text available
This paper presents a synthesis of ImageCLEF 2013 plant identification task, a system-oriented testbed dedicated to the evaluation of image-based plant identification technologies. With 12 participating groups coming from over 9 countries and 33 submitted runs, the 2013 campaign confirmed the increasing interest of the multimedia community in ecolo...
Conference Paper
Full-text available
Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to...
Conference Paper
Full-text available
There has recently been increasing interest in using advanced computer vision techniques for automatic plant identification. Most of the approaches proposed are based on an analysis of leaf characteristics. Nevertheless, two aspects have still not been well exploited: (1) domain-specific or botanical knowledge (2) the extraction of meaningful and r...
Article
Speeding up the collection and integration of raw botanical observation data is a crucial step towards a sustainable development of agriculture and the conservation of biodiversity. Initiated in the context of a citizen sciences project, the main contribution of this paper is an innovative collaborative workflow focused on image-based plant identif...
Conference Paper
Full-text available
Unravelling mysteries of the diversity of the plant community is a crucial issue both for the development of many botanical industries as well as for the conservation of ecosystem biodiversity. Traditionally, botanists have proposed detailed dichotomous key descriptions (called also characters or concepts) about the morphology of plants and particu...
Conference Paper
We study fine-grained categorization, the task of distinguishing among (sub)categories of the same generic object class (e.g., birds), focusing on determining botanical species (leaves and orchids) from scanned images. The strategy is to focus attention around several vantage points, which is the approach taken by botanists, but using features dedi...
Conference Paper
Automatic retrieval tools are becoming increasingly important in botany and agriculture due to the growing interest in biodiversity and the ongoing shortage of skilled taxonomists. Our work is motivated by a botanical field scenario where the basic unit of observation is a plant. We describe a novel, image-based retrieval system for both educationa...
Article
Full-text available
This article summarizes the outcome of the 2012 Shonan Meeting “Future of Multimedia Analysis and Mining.”The meeting was really interesting, and the participants had a fun time with an Kamakura excursion and fine dinners, in addition to in-depth discussions on ready-to-go hot research topics (see Figure 4). We have enjoyed sharing even part of our...
Conference Paper
Efficiently constructing the K-Nearest Neighbor Graph (K-NNG) of large and high dimensional datasets is crucial for many applications with feature-rich objects, such as images or other multimedia content. In this paper we investigate the use of high dimensional hashing methods for efficiently approximating the K-NNG in distributed environments. We...
Conference Paper
Full-text available
This paper presents a new interactive web application for the visual identification of plants based on collaborative pictures. Contrary to previous content-based identification methods and systems developed for plants that mainly relied on leaves, or in few other cases on flowers, it makes use of five different organs and plant's views including ha...
Conference Paper
Full-text available
Automatic plant identification is a relatively new research area in computer vision that has increasingly attracted high interest as a promising solution for the development of many botanical industries and for the success of biodiversity conservation. Most of the approaches proposed are based on the analysis of morphological properties of leaves....
Conference Paper
Full-text available
The problem of automatic leaf identification is particularly challenging because, in addition to constraints derived from image processing such as geometric deformations (rotation, scale, translation) and illumination variations, it involves difficulties arising from foliar properties. These include two main aspects: the first is the enormous numbe...
Article
Efficiently constructing the K-Nearest Neighbor Graph (K-NNG) of large and high dimensional datasets is crucial for many applications with feature-rich objects, such as images or other multimedia content. In this paper we investigate the use of high dimensional hashing methods for efficiently approximating the K-NNG, notably in distributed environm...
Article
We propose a new supervised object retrieval method based on the selection of local visual features learned with the BLasso algorithm. BLasso is a boosting-like procedure that efficiently approximates the Lasso path through backward regularization steps. The advantage compared to a classical boosting strategy is that it produces a sparser selection...
Article
Full-text available
Humans usually describe objects along a certain direction, called intuitive direction, in other words place them in a way that they are commonly seen in their surroundings. In computer vision, the intuitive alignment may be very useful for object interpretation and semantic classification. For example, it may facilitate the extraction of characteri...
Conference Paper
Full-text available
Leaf morphological characters are a useful visual guide for constructing relationships between different plants and between plants and their environment. However, extracting and analysing these characters are carried out manually by botanists, which is a painstaking and time-consuming task. One way to accelerate and broaden the use of these charact...
Article
In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason m...
Conference Paper
Full-text available
This demo presents a crowdsourcing web application dedicated to the access of botanical knowledge through automated identification of plant species by visual content. Inspired by citizen sciences, our aim is to speed up the collection and integration of raw botanical observation data, while providing to potential users an easy and efficient access...
Conference Paper
We propose a higher order conditional random field built over a graph of superpixels for partitioning natural images into coherent segments. Our model operates at both superpixel and segment levels and includes potentials that capture similarity, proximity, curvilinear continuity and familiar configuration. For a given image, these potentials enfor...
Conference Paper
Full-text available
Understanding the results returned by automatic visual concept detectors is often a tricky task making users uncomfortable with these technologies. In this paper we attempt to build humanly interpretable visual models, allowing the user to visually understand the underlying semantic. We therefore propose a supervised multiple instance learning algo...
Conference Paper
Full-text available
Organizing media according to real-life events is attracting interest in the multimedia community. Event-centric indexing approaches are very promising for discovering more complex relationships between data. In this paper we introduce a new visual-based method for retrieving events in photo collections, typically in the context of User Generated C...
Conference Paper
Full-text available
Image CLEF' plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. This paper presents an overview of the resources and assessments of...
Article
Full-text available
Shared Nearest Neighbours (SNN) techniques are well known to overcome several shortcomings of traditional clustering approaches, notably high dimensionality and metric limitations. However, previous methods were limited to a single information source whereas such methods appear to be very well suited for heterogeneous data, typically in multi-modal...
Conference Paper
In the context of computer-assisted plant identification we are facing challenging information retrieval problems because of the very high within-class variability and of the limited number of training examples. To address these problems, we suggest a new interactive learning approach that combines similarity-based retrieval and re-ranking by SVM u...
Conference Paper
Query by visual example (QBVE) has been widely exploited in image retrieval. If starting image is missing, the query by visual thesaurus paradigm allows the user to compose his mental query image through visual patches summarizing the region database. Researches on the human visual system have provided considerable evidence that the color and textu...
Conference Paper
Multimedia search engines are often based on multiple decentralized search services, multiple information sources (text search, audio search, visual search, semantic search engines, etc.), multiple data representation and similarity measures. Heterogeneous multiple search results need to be combined and structured efficiently and generically. In th...
Article
The need of summarization methods and systems has become more and more crucial as the audio-visual material continues its critical growth. This paper presents a novel vision and a novel system for movies summarization. A video summary is an audio-visual document displaying the essential parts of an original document. However, the definition of the...
Article
The need for watching movies is in perpetual increase due to the widespread of the internet and the increasing popularity of the video on demand service. The important mass of movies stored in the Internet or in VOD servers need to be structured to accelerate the browsing operation. In this paper, we propose a new system called "The Scene Pathfinde...
Article
This paper presents an efficient approach for copy detection in large archives containing several hundred hours of videos, called ViCopT for Video Copy Tracking. Our video content indexing method consists in computing trends of behaviors of points of interest and then to assign them a label of behavior. Two methods are proposed to assign the labels...
Conference Paper
Full-text available
This paper presents an efficient local features boosting strategy for interactive objects retrieval tasks such as on-line supervised learning or relevance feedback. The prediction time complexity of most existing methods is indeed usually linear in dataset size since the retrieval works by applying a trained classifier on the images of the dataset...
Conference Paper
Full-text available
The bag-of-visual-words is a popular representation for images that has proven to be quite effective for automatic annotation. In this paper, we extend this representation in order to include weak geometrical information by using visual word pairs. We show on a standard benchmark dataset that this new image representation improves significantly the...
Conference Paper
In this paper, we present a probabilistic framework for urban area extraction in remote sensing images using a conditional random field built over an adjacency graph of superpixels. Our discriminative model performs a multi-cue combination by incorporating efficiently color, texture and edge cues. Both local and pairwise feature functions are learn...
Conference Paper
We propose a non-homogeneous conditional random field built over an adjacency graph of superpixels for contextual classification of high-resolution satellite images. By introducing the contextual histogram descriptor, our model includes spatially dependent unary and pairwise potentials that capture contextual interactions of the data as well as the...
Article
Full-text available
The success of the bag-of-words approach for text has inspired the recent use of analogous strategies for global represen-tation of images with local visual features. Many applications have been proposed for object detection, image annotation, queries-by-example, relevance feedback, automatic annotation, and clustering. In this paper, we investigat...
Article
Full-text available
The third and last CHORUS conference on MMSE took place from the 26 th to the 27 th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the world.
Article
Full-text available
This paper describes the participation of VITALAS in the TRECVID-2009 evaluation where we submitted runs for the High-Level Feature Extraction (HLFE) and Interactive Search tasks. For the HLFE task, we focus on the evaluation of low-level feature sets and fusion methods. The runs employ multiple low-level features based on all available modalities...
Conference Paper
We propose a non-homogeneous conditional random field (CRF) built over an adjacency graph of superpixels for contextual region grouping. Our model includes spatially dependent potentials that capture contextual interactions of the data as well as the labels. Both superpixels and segments are described with local statistics which take into account t...
Conference Paper
In this paper, we present a probabilistic framework for edge and region grouping using conditional random field. Our model is built on a hybrid adjacency graph of atomic region and contour primitives. Unary and pairwise potentials that capture similarity, proximity and curvilinear continuity are defined. Similarity, for both region and edge cues, i...
Article
Many image databases available today have keyword annotations associated with images. State of the art low-level visual features reflect well the "physical" content and thus the visual similarity between images, but retrieval based on visual features alone is subject to the semantic gap. Alternatively, text annotations can be linked to image contex...
Article
In this paper, we present an approach to learning latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: (1) ambiguous correspondences between visual features and annotated keywords; (2) incomplete lists of annotated keywords. The second rea...
Article
Clustering algorithms are increasingly employed for the categorization of image databases, in order to provide users with database overviews and make their access more effective. By including information provided by the user, the categorization process can produce results that come closer to user's expectations. To make such a semi-supervised categ...
Article
We address the challenge of semantic gap reduction for image retrieval through an improved SVM-based active relevance feedback framework, together with a hybrid visual and conceptual content representation and retrieval. We introduce a new feature vector based on projecting the keywords associated to an image on a set of "key concepts" with the hel...
Chapter
In comparison with the classic query-by-example paradigm, the “mental image search” paradigm lifts the strong assumption that the user has a relevant example at hand to start the search. In this chapter, we review different methods that implement this paradigm, originating from both the content-based image retrieval and the object recognition field...
Book
This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Adaptive Multimedia Retrieval, AMR 2007, held in Paris, France, in July 2007. The 18 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement. The papers are organize...
Article
Full-text available
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socioeconomic perspective. The technical perspec...
Conference Paper
Full-text available
This paper deals with a new interest points detector. Unlike most standard detectors which concentrate on the local shape of the signal, the main objective of this new operator is to extract interpretable points from the image context. The basic principle of this operator was the detection of radial symmetries, but we have generalized it to cover o...