TEXTURE ANALYSIS VIA HIERARCHICAL SPATIAL-SPECTRAL CORRELATION (HSSC)

被引：0

作者：

Zhang, Kaitai ^{[1
]}

Chen, Hong-Shuo ^{[3
]}

Wang, Ye

Ji, Xiangyang ^{[2
]}

Kuo, C. -C. Jay ^{[1
]}

机构：

[1] Univ Southern Calif, Ming Heish Dept Elect Engn, Los Angeles, CA 90007 USA

[2] Tsinghua Univ, Dept Automat, Beijing, Peoples R China

[3] Natl Chiao Tung Univ, Coll Elect & Comp Engn, Hsinchu, Taiwan

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2019年

基金：

国家自然科学基金国际合作与交流项目;

关键词：

Texture analysis; texture classification; spatial-spectral transform; spatial-spectral correlation; neural-network-inspired image transform; SEGMENTATION; CLASSIFICATION;

D O I：

10.1109/icip.2019.8803556

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

A hierarchical spatial-spectral correlation (HSSC) method is proposed for texture analysis in this work. The HSSC method first applies a multi-stage spatial-spectral transform to input texture patches, which is known as the Saak transform. Then, it conducts a correlation analysis on Saak transform coefficients to obtain texture features of high discriminant power. To demonstrate the effectiveness of the HSSC method, we conduct extensive experiments on texture classification and show that it offers very competitive results comparing with state-of-the-art methods.

引用

页码：4419 / 4423

页数：5

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