Bag of Systems Trees


The bag-of-systems (BoS) representation is a descriptor of motion in a video, where dynamic texture (DT) codewords represent the typical motion patterns in spatio-temporal patches extracted from the video. The efficacy of the BoS descriptor depends on the richness of the codebook, which depends on the number of codewords in the codebook. However, for even modest sized codebooks, mapping videos onto the codebook results in a heavy computational load. In this project we propose the BoS Tree, which constructs a bottom-up hierarchy of codewords that enables efficient mapping of videos to the BoS codebook. By leveraging the tree structure to  efficiently index the codewords, the BoS Tree allows for fast look-ups in the codebook and enables the practical use of larger, richer codebooks. We demonstrate the effectiveness of BoS Trees on classification of different video datasets, as well as on annotation of a video dataset and a music dataset.  We also show that, although the fast look-ups of BoS Tree result in different descriptors than BoS for the same video, the overall distance (and kernel) matrices are highly correlated resulting in similar classification performance.

Selected Publications