Chaotic Features for Dynamic Textures Recognition with Group Sparsity Representation
Xinbin Luo, Shan Fu, Yong Wang · KSII Transactions on Internet and Information Systems · 2015
Dynamic texture (DT) recognition is a challenging problem in numerous applications.In this study, we propose a new algorithm for DT recognition based on group sparsity structure in conjunction with chaotic feature vector.Bag-of-words model is used to represent each video as a histogram of the chaotic feature vector, which is proposed to capture self-similarity property of the pixel intensity series.The recognition problem is then cast to a group sparsity model, which can be efficiently optimized through alternating direction method of multiplier algorithm.Experimental results show that the proposed method exhibited the best performance among several well-known DT modeling techniques.