Fig 4 - uploaded by Stefanos Vrochidis
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Results of a textual query with the keyword "water". 1 Demo available at: http://mklab-services.iti.gr/lelantus 2 TRECVID: http://www-nlpir.nist.gov/projects/trecvid/
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This paper describes a video retrieval search engine that exploits both video analysis and user implicit feedback. Video analysis (i.e. automatic speech recognition, shot segmentation and keyframe processing) is performed by employing state of the art techniques, while for implicit feedback analysis we propose a novel methodology, which takes into...
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... the improvement of the performance, when implicit feedback is taken into account, we present visual examples from interaction modes, as well as evaluation of the results utilizing precision and recall metrics. First, we present a usage scenario, in which the user is looking for scenes that a water body is visible by typing the keyword "water" (Fig. 4). As text retrieval is performed on the noisy information provided by Automatic Speech Recognition (ASR), only some of the results depict water scenes. Conducting the same query utilizing the graph with the past interaction data, we get a clearly better set of results (Fig. 5). Performance in terms of precision and recall for the 2 nd ...
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Citations
This paper describes an approach to exploit the implicit user feedback gathered during interactive video retrieval tasks. We propose a framework, where the video is first indexed according to temporal, textual, and visual features and then implicit user feedback analysis is realized using a graph-based methodology. The generated graph encodes the semantic relations between video segments based on past user interaction and is subsequently used to generate recommendations. Moreover, we combine the visual features and implicit feedback information by training a support vector machine classifier with examples generated from the aforementioned graph in order to optimize the query by visual example search. The proposed framework is evaluated by conducting real-user experiments. The results demonstrate that significant improvement in terms of precision and recall is reported after the exploitation of implicit user feedback, while an improved ranking is presented in most of the evaluated queries by visual example.
