Conference Paper

An eye-tracking-based approach to facilitate interactive video search

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Abstract

This paper investigates the role of gaze movements as implicit user feedback during interactive video retrieval tasks. In this context, we use a content-based video search engine to perform an interactive video retrieval experiment, during which, we record the user gaze movements with the aid of an eye-tracking device and generate features for each video shot based on aggregated past user eye fixation and pupil dilation data. Then, we employ support vector machines, in order to train a classifier that could identify shots marked as relevant to a new query topic submitted by new users. The positive results provided by the classifier are used as recommendations for future users, who search for similar topics. The evaluation shows that important information can be extracted from aggregated gaze movements during video retrieval tasks, while the involvement of pupil dilation data improves the performance of the system and facilitates interactive video search.

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... For instance, if a user, who performs interactive video search, views a video directly after she has submitted the query Bcar^, it is highly possible that this video contains a car and could be annotated using this tag. Towards to exploiting this fact, several research works rely upon graph-based representations, in order to link viewed or clicked items with queries [12,13,38], while others focus on identifying user interest by considering gaze movements [18,39] and predefined (known) topics. ...
... Recently, approaches for performing relevance feedback based on eye features are proposed in [44] and [23], while a gaze-based relevance feedback approach for region-based image search is presented in [30]. In [39] the authors propose the generation of recommendations based on a SVM classifier trained with fixation and pupil dilation based features, while [40] extends this work by performing query clustering based on dominant sets. Other recent works in image retrieval attempt to combine image features with eye movements, either by using a ranking SVM approach [10], or by identifying areas of interest in an image to extract local visual features [24]. ...
... These works focus on more controlled retrieval scenarios and do not deal with predicting the relevance of results for a new query by a new user [18], they do not consider unknown search topics [39] and they do not incorporate gaze movements to support query clustering [40]. Also, none of them considers combination of gaze movements and click-through data for video retrieval e.g., [1,23,30] focus on image search). ...
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In the recent years, the rapid increase of the volume of multimedia content has led to the development of several automatic annotation approaches. In parallel, the high availability of large amounts of user interaction data, revealed the need for developing automatic annotation techniques that exploit the implicit user feedback during interactive multimedia retrieval tasks. In this context, this paper proposes a method for automatic video annotation by exploiting implicit user feedback during interactive video retrieval, as this is expressed with gaze movements, mouse clicks and queries submitted to a content-based video search engine. We exploit this interaction data to represent video shots with feature vectors based on aggregated gaze movements. This information is used to train a classifier that can identify shots of interest for new users. Subsequently, we propose a framework that during testing: a) identifies topics (expressed by query clusters), for which new users are searching for, based on a novel clustering algorithm and b) associates multimedia data (i.e., video shots) to the identified topics using supervised classification. The novel clustering algorithm is based on random forests and is driven by two factors: first, by the distance measures between different sets of queries and second by the homogeneity of the shots viewed during each query cluster defined by the clustering procedure; this homogeneity is inferred from the performance of the gaze-based classifier on these shots. The evaluation shows that the use of aggregated gaze data can be exploited for video annotation purposes.
... In the related literature, implicit tagging has been used for direct annotation of data (such as images, video and music) with predefined sets of implicit tags (such as affective labels for describing emotion elicitation) [34] [38], assessment of explicit tag quality and correctness [1] [15] [33], user profiling by tracking personal preferences [11] and content summarization based on implicitly obtained feedback used mainly for re-ranking of results [41]. We limit our documentation of related work to research concerning the tagging of visual content such as images and videos, as they are more relevant to our image-oriented approach and experiments described in this paper, but we encourage readers to refer to the works of [2] [4] [28] concerning topical relevance of textual search results, as well as research on implicit characterization of musical scores [29], for a complete overview on emerging methodologies concerning IHCT. ...
... More specifically, the authors are able to extract a level of interest ranging from 0 (no interest) to 1 (fully interested) using a fuzzy logic based gaze inference system, reporting an accuracy of 53%. The potential of exploiting implicit gaze feedback data for the improvement of query-specific recommendations for movie clips is also explored in Vrochidis et al [41]. In their experiments using a content-based video search engine, they recorded past user gaze fixation and pupil dilation data using an eye tracking system in order to generate a set of features that describes each video being gazed at. ...
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In this paper, a framework for implicit human-centered tagging is presented. The proposed framework draws its inspiration from the psychologically established process of attribution. The latter strives to explain affect-related changes observed during an individual's participation in an emotional episode, by bestowing the corresponding affect changing properties on a selected perceived stimulus. Our framework tries to reverse-engineer this attribution process. By monitoring the annotator's focus of attention through gaze-tracking, we identify the stimulus attributed as the cause for the observed change in core affect. The latter is analyzed from the user's facial expressions. Experimental results attained by a lightweight, cost-efficient application based on the proposed framework show promising accuracy in both the assessment of topical relevance and direct annotation scenarios. These results are especially encouraging given the fact that the behavioral analyzers used to obtain user affective response and eye gaze lack the level of sophistication and high cost usually encountered in the related literature.
... As for pupil velocity, we also generated the mean, stdev, min and max. Previous work has used pupil velocity to infer users' search intentions in video retrieval tasks [56], as well as reading comprehension [44]. To account for potential physiological differences in pupil size among individual users, measured pupil dilation values for each user are adjusted with respect to their baseline using the percentage change in pupil size (PCPS), reported in µm, which [32] defines as: measured pupilsize − baseline pupilsize baseline pupilsize ...
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