AUTOMATICWEB VIDEO CATEGORIZATION USING AUDIO-VISUAL INFORMATION AND HIERARCHICAL CLUSTERING RELEVANCE FEEDBACK

Bogdan Ionescu (1) , Klaus Seyerlehner (2) , Ionut Mironica (1) , Constantin Vertan (1) , Patrick Lambert (3)

1 LAPI - Laboratorul de Analiza si Prelucrarea Imaginilor [Bucarest]
2 Department of Computational Perception
3 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance

In this paper, we discuss and audio-visual approach to automatic web video categorization. We propose content descriptors which exploit audio, temporal, and color content. The power of our descriptors was validated both in the context of a classification system and as part of an information retrieval approach. For this purpose, we used a real-world scenario, comprising 26 video categories from the blip.tv media platform (up to 421 hours of video footage). Additionally, to bridge the descriptor semantic gap, we propose a new relevance feedback technique which is based on hierarchical clustering. Experiments demonstrated that retrieval performance can be increased significantly and becomes comparable to that of high level semantic textual descriptors.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Bogdan Ionescu, Klaus Seyerlehner, Ionut Mironica, Constantin Vertan, Patrick Lambert. AUTOMATICWEB VIDEO CATEGORIZATION USING AUDIO-VISUAL INFORMATION AND HIERARCHICAL CLUSTERING RELEVANCE FEEDBACK. 20th European Signal Processing Conference - EUSIPCO 2012, Aug 2012, Romania. pp.1-5. ⟨hal-00732732⟩