Dynamic Texture Recognition Using Multiscale Binarized Statistical Image Features

Abstract

A spatio-temporal descriptor for representation and recognition of time-varying textures is proposed [binarized statistical image features on three orthogonal planes (BSIF-TOP)] in this paper. The descriptor, similar in spirit to the well known local binary patterns on three orthogonal planes approach, estimates histograms of binary coded image sequences on three orthogonal planes corresponding to spatial/spatio-temporal dimensions. However, unlike some other methods which generate the code in a heuristic fashion, binary code generation in the BSIF-TOP approach is realized by filtering operations on different regions of spatial/spatio-temporal support and by binarizing the filter responses. The filters are learnt via independent component analysis on each of three planes after preprocessing using a whitening transformation. By extending the BSIF-TOP descriptor to a multiresolution scheme, the descriptor is able to capture the spatio-temporal content of an image sequence at multiple scales, improving its representation capacity. In the evaluations on the UCLA, Dyntex, and Dyntex++ dynamic texture databases, the proposed method achieves very good performance compared to existing approaches.