Dynamie texture classification using local fuzzy coding

Recognition of complex dynamic texture is a challenging problem and captures the attention of the computer vision community for several decades. Essentially the dynamic texture recognition is a multi-class classification problem that has become a real challenge for computer vision and machine learning techniques. In this paper, we propose a new approach to tackle the dynamic texture recognition problem. First, we utilize the fuzzy clustering technology to design a fuzzy codebook, and then construct a soft assigned local fuzzy coding feature to represent the whole dynamic texture sequence. This new coding strategy preserves spatial and temporal characteristics of dynamic texture. Finally, by evaluating the proposed approach using with the DynTex dataset, we show the effectiveness of the proposed local fuzzy coding strategy.