Combining spatial and temporal patches for scalable video indexing

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 exh...

• Type: Journal
• Year: 2010
• Where: MTA
• Authors: Paolo Piro, Sandrine Anthoine, Eric Debreuve, Michel Barlaud
• Keywords: Hardware, Motion Patches, MTA 2010, Scalable Video Indexing, Spatial