Chaotic Features for Dynamic Textures Recognition with Group Sparsity Representation

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 chao...

Full description

Saved in:
Bibliographic Details
Published in:KSII transactions on Internet and information systems 2015-11, Vol.9 (11), p.4556-4572
Main Authors: Luo, Xinbin, Fu, Shan, Wang, Yong
Format: Article
Language:Korean
Subjects:
chaotic feature vector
Dynamic textures recognition
group sparsity
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1976-7277
1976-7277