An approach to texture segmentation analysis based on sparse coding model and EM algorithm

Authors:
Lijuan Duan;Jicai Ma;Zhen Yang;Jun Miao
Affiliations:
College of Computer Science and Technology, Beijing University of Technology, Beijing, China;College of Computer Science and Technology, Beijing University of Technology, Beijing, China;College of Computer Science and Technology, Beijing University of Technology, Beijing, China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

Sparse coding theory is a method for finding a reduced representation of multidimensional data When applied to images, this theory can adopt efficient codes for images that captures the statistically significant structure intrinsic in the images In this paper, we mainly discuss about its application in the area of texture images analysis by means of Independent Component Analysis Texture model construction, feature extraction and further segmentation approaches are proposed respectively The experimental results demonstrate that the segmentation based on sparse coding theory gets promising performance.