Combining spatial and temporal patches for scalable video indexing

Abstract : This paper tackles the problem of scalable video indexing. We propose a new framework combining spatial and motion patch descriptors. The spatial descriptors are based on a multiscale description of the image and are called Sparse Multiscale Patches. We propose motion patch descriptors based on block motion that describe the motion in a Group of Pictures. The distributions of these sets of patches are compared combining weighted Kullback-Leibler divergences between spatial and motion patches. These divergences are estimated in a non-parametric framework using a k-th Nearest Neighbor estimator. We evaluate this weighted dissimilarity measure on selected videos from the ICOS-HD ANR project. Experiments show that the spatial part of the measure is relevant to detect different sequences, while its motion part allows to detect clips within a sequence. Experiments combining the spatial and temporal parts of the dissimilarity measure show its robustness to resampling and compression; thus exhibiting the spatial scalability of the method on heterogeneous networks.
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Submitted on : Tuesday, September 29, 2009 - 6:13:16 PM
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Paolo Piro, Sandrine Anthoine, Eric Debreuve, Michel Barlaud. Combining spatial and temporal patches for scalable video indexing. Multimedia Tools and Applications, Springer Verlag, 2009, pp.1. ⟨10.1007/s11042-009-0350-4⟩. ⟨hal-00420850⟩

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