Dynamic Texture Analysis and Classification Using Deterministic Partially Self-avoiding Walks

摘要

Dynamic texture has been attracting extensive attention in the field of computer vision in the last years. These patterns can be described as moving textures which the idea of self-similarity presented by static textures is extended to the spatio-temporal domain. Although promising results have been achieved by recent methods, most of them cannot model multiple regions of dynamic textures and/or both motion and appearance features. To overcome these drawbacks, a novel approach for dynamic texture modeling based on deterministic partially self-avoiding walks is proposed. In this method, deterministic partially self-avoiding walks are performed in three orthogonal planes to combine appearance and motion features of the dynamic textures. Experimental results on two databases indicate that the proposed method improves correct classification rate compared to the existing methods.