Browsing by Author "Chen, Hong-Shuo"

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A DATA-CENTRIC APPROACH TO UNSUPERVISED TEXTURE SEGMENTATION USING PRINCIPLE REPRESENTATIVE PATTERNS
(2019-01-01)
Zhang, Kaitai
;
Chen, Hong-Shuo
;
Zhang, Xinfeng
;
Wang, Ye
;
Kuo, C. -C. Jay
Features that capture textural patterns of a certain class of images are crucial for texture segmentation tasks. This paper introduces a data-centric approach to efficiently extract and represent textural information, which adapts to a wide variety of textures. Based on the strong self-similarities and quasi periodicity in texture images, the proposed method first constructs a representative texture pattern set for the given image by leveraging the patch clustering strategy. Then, pixel wise texture features are designed according to the similarities between local patches and the representative textural patterns. Moreover, the proposed feature is generic and flexible, and can perform segmentation task by integrating it into various segmentation approaches easily. Extensive experimental results on both textural and natural image segmentation show that the segmentation method using the proposed features achieves very competitive or even better performance compared with the stat-of-the-art methods.
TEXTURE ANALYSIS VIA HIERARCHICAL SPATIAL-SPECTRAL CORRELATION (HSSC)
(2019-01-01)
Zhang, Kaitai
;
Chen, Hong-Shuo
;
Wang, Ye
;
Ji, Xiangyang
;
Kuo, C. -C. Jay
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.