Skip to Main content Skip to Navigation
Journal articles

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.
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download
Contributor : Sandrine Anthoine <>
Submitted on : Tuesday, September 29, 2009 - 6:13:16 PM
Last modification on : Tuesday, December 8, 2020 - 9:39:58 AM
Long-term archiving on: : Wednesday, June 16, 2010 - 12:13:00 AM


Files produced by the author(s)




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⟩



Record views


Files downloads