Texture Classification Using Scattering Statistical and Cooccurrence Features. (8th February 2016)

• Record Type: Journal Article
• Authors: Wang, Juan; Zhang, Jiangshe; Zhao, Jie
• Other Names: Solimene Raffaele Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2016(2016)
• Publication Date: 2016-02-08
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2016/3946312
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Texture classification is an important research topic in image processing. In 2012, scattering transform computed by iterating over successive wavelet transforms and modulus operators was introduced. This paper presents new approaches for texture features extraction using scattering transform. Scattering statistical features and scattering cooccurrence features are derived from subbands of the scattering decomposition and original images. And these features are used for classification for the four datasets containing 20, 30, 112, and 129 texture images, respectively. Experimental results show that our approaches have the promising results in classification.