Sparse binarised statistical dynamic features for spatio-temporal texture analysis

Abstract

The paper presents a new spatio-temporal learning-based descriptor called binarised statistical dynamic features (BSDF) for representation and classification of dynamic texture. The BSDF descriptor operates by applying three-dimensional spatiotemporal filters on local voxels of an image sequence where the filters are learned via an independent component analysis, maximising independence over spatial and temporal domains concurrently. The BSDF representation is formed by binarising filter responses which are then converted into codewords and summarised using histograms. A robust representation of the BSDF descriptor is finally obtained via a sparse representation approach yielding very discriminative features for classification. The effects of different hyper-parameters on performance including the number of filters, the number of scales, temporal depth, number of samples drawn are also investigated. The proposed approach is evaluated on the most commonly used dynamic texture databases and shown to perform very well compared to the existing methods.