An improved local descriptor and threshold learning for unsupervised dynamic texture segmentation
Dynamic texture (DT) is an extension of texture to the temporal domain. How to segment DTs is a challenging problem. In this paper, we propose significant improvements to a recently published DT segmentation method. We employ a new spatiotemporal local texture descriptor which combines local binary patterns with a differential excitation measure. We also address the important problem of threshold selection by proposing a method for determining thresholds for the segmentation method by statistical learning. An improved criterion for merging adjacent regions is also introduced. Experimental results show that our approach provides very good segmentation results compared to state-of-the-art methods.