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Spatio-Temporal Tube data representation and Kernel design for SVM-based video object retrieval system

Shuji Zhao 1 Frédéric Precioso 1 Matthieu Cord 2
1 MIDI - Multimedia Indexation and Data Integration
ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In this article, we propose a new video object retrieval system. Our approach is based on a Spatio-Temporal data representation, a dedicated kernel design and a statistical learning toolbox for video object recognition and retrieval. Using state-of-the-art video object detection algorithms (for faces or cars, for example) we segment video object tracks from real movies video shots. We then extract, from these tracks, sets of spatio-temporally coherent features that we call Spatio-Temporal Tubes. To compare these complex tube objects, we design a Spatio-Temporal Tube Kernel (STTK) function. Based on this kernel similarity we present both supervised and active learning strategies embedded in Support Vector Machine framework. Additionally, we propose a multi-class classification framework dealing with unbalanced data. Our approach is successfully evaluated on two real movies databases, the french movie “L’esquive” and episodes from “Buffy, the Vampire Slayer” TV series. Our method is also tested on a car database (from real movies) and shows promising results for car identification task.
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Submitted on : Friday, January 11, 2013 - 2:48:55 PM
Last modification on : Monday, January 25, 2021 - 3:16:02 PM

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Shuji Zhao, Frédéric Precioso, Matthieu Cord. Spatio-Temporal Tube data representation and Kernel design for SVM-based video object retrieval system. Multimedia Tools and Applications, Springer Verlag, 2011, Special issue on "Image and Video Retrieval: Theory and Applications"., 55 (1), pp.105-125. ⟨10.1007/s11042-010-0602-3⟩. ⟨hal-00773031⟩



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