Abstract
This paper proposes an approach for indexing large collections of videos, dedicated to content-based copy detection. The visual description chosen involves local descriptors based on interest points. Firstly, we propose the joint use of different natures of spatial supports for the local descriptors. We will demonstrate that this combination provides a more representative and then a more informative description of each frame. As local supports, we use the classical Harris detector, added to a detector of local symmetries which is inspired by pre-attentive human vision and then expresses a strong semantic content. Our second contribution consists in enriching such descriptors by characterizing their dynamic behavior in the video sequence: estimating the trajectories of the points along frames allows to highlight trends of behaviors, and then to assign a label of behavior to each local descriptor. The relevance of our approach is evaluated on several hundred hours of videos, with severe attacks. The results obtained clearly demonstrate the richness and the compactness of the new spatio-temporal description proposed.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hampapur, A., Bolle, R.: Comparison of sequence matching techniques for video copy detection. In: Conf. on Storage and Retrieval for Media Databases (2002)
Joly, A., Frelicot, C., Buisson, O.: Feature statistical retrieval applied to content-based copy identification. In: ICIP (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: ICPR (2003)
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003)
Opelt, A., Sivic, J., Pinz, A.: Generic object recognition from video data. In: 1st Cognitive Vision Workshop (2005)
Law-To, J., Gouet-Brunet, V., Buisson, O., Boujemaa, N.: Local Behaviours Labelling for Content Based Video Copy Detection. In: ICPR, Hong-Kong (2006)
Harris, C., Stevens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conference, pp. 153–158 (1988)
Reisfeld, D., Wolfson, H., Yeshurun, Y.: Context free attentional operators: The generalized symmetry transform. IJCV, Special Issue on Qualitative Vision (1994)
Privitera, C.M., Stark, L.W.: Algorithms for defining visual regions-of-interest: Comparison with eye fixations. PAMI 22, 970–982 (2000)
Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)
Stentiford, F.W.M.: Attention based symmetry in colour images. In: IEEE Int. Workshop on Multimedia Signal Processing (2005)
Locher, P., Nodine, C.: Symmetry Catches the Eye. In: O’Regan, J., Levy-Schoen, A. (eds.) Eye Movements: From Physiology to Cognition. Elsevier Science Publishers B.V, Amsterdam (1987)
Lin, C.-C., Lin, W.-C.: Extracting facial features by an inhibitory mechanism based on gradient distributions. Pattern Recognition 29 (1996)
Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical report CMU-CS-91-132 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Law-To, J., Gouet-Brunet, V., Buisson, O., Boujemaa, N. (2006). Labeling Complementary Local Descriptors Behavior for Video Copy Detection. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_39
Download citation
DOI: https://doi.org/10.1007/11848035_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
Online ISBN: 978-3-540-39393-1
eBook Packages: Computer ScienceComputer Science (R0)