Robust voting algorithm based on labels of behavior for video copy detection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Object recognition and segmentation in videos by connecting heterogeneous visual features
Computer Vision and Image Understanding
Video sequence matching based on temporal ordinal measurement
Pattern Recognition Letters
Visual words based spatiotemporal sequence matching in video copy detection
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Video copy detection using multiple visual cues and MPEG-7 descriptors
Journal of Visual Communication and Image Representation
Real-time large scale near-duplicate web video retrieval
Proceedings of the international conference on Multimedia
Labeling complementary local descriptors behavior for video copy detection
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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This paper presents an approach for indexing a large set of videos by considering the dynamic behaviour of local visual features along the sequences. The proposed concept is based on the extraction and the local description of interest points and further on the estimation of their trajectories along the video sequence. Analysing the low-level description obtained allows to highlight trends of behaviour and then to assign a label. Such an indexing approach of the video content has several interesting properties: the lowlevel descriptors provide a rich and compact description, while labels of behaviour provide a generic semantic description of the video content, relevant for video content retrieval. We demonstrate the effectiveness of this approach for Content-Based Copy Detection (CBCD) on large collections of videos (several hundred hours of videos).