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Texture Characterization Using Shape Co-Occurrence Patterns

发布日期:2017-10-12 15:32:25 阅读次数:[871]次 作者:

核心提示:来源出版物: IEEE TRANSACTIONS ON IMAGE PROCESSING

作者: Xia, GS (Xia, Gui-Song); Liu, G (Liu, Gang); Bai, X (Bai, Xiang); Zhang, LP (Zhang, Liangpei)

来源出版物: IEEE TRANSACTIONS ON IMAGE PROCESSING  卷: 26  期: 10  页: 5005-5018  DOI: 10.1109/TIP.2017.2726182  出版年: OCT 2017  

摘要: Texture characterization is a key problem in image understanding and pattern recognition. In this paper, we present a flexible shape-based texture representation using shape co-occurrence patterns. More precisely, texture images are first represented by a tree of shapes, each of which is associated with several geometrical and radiometric attributes. Then, four typical kinds of shape co-occurrence patterns based on the hierarchical relationships among the shapes in the tree are learned as codewords. Three different coding methods are investigated for learning the codewords, which can be used to encode any given texture image into a descriptive vector. In contrast with existing works, the proposed approach not only inherits the shape-based method's strong ability to capture geometrical aspects of textures and high robustness to variations in imaging conditions but also provides a flexible way to consider shape relationships and to compute high-order statistics on the tree. To the best of our knowledge, this is the first time that co-occurrence patterns of explicit shapes have been used as a tool for texture analysis. Experiments on various texture and scene data sets demonstrate the efficiency of the proposed approach.

入藏号: WOS:000406993600008

作者识别号:

作者ResearcherID 号ORCID 号Xia, Gui-Song  0000-0001-7660-6090 

ISSN: 1057-7149

eISSN: 1941-0042

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