TEXTURE ANALYSIS WITH SHAPE CO-OCCURRENCE PATTERNS

被引:14
作者
Liu, Gang [1 ,3 ]
Xia, Gui-Song [1 ]
Yang, Wen [2 ]
Zhang, Liangpei [1 ]
机构
[1] Wuhan Univ, State Key Lab LIESMARS, Wuhan 430079, Peoples R China
[2] Wuhan Univ, Elect Informat Sch, Wuhan 430079, Peoples R China
[3] Telecom ParisTech, CNRS LTCI, F-75013 Paris, France
来源
2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2014年
关键词
CLASSIFICATION;
D O I
10.1109/ICPR.2014.288
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a flexible shape-based texture analysis method by investigating the co-occurrence patterns of shapes. More precisely, a texture image is represented by a tree of shapes, each of which is associated with several attributes. The modeling of texture is thus converted to characterize the tree of shapes. To this aim, we first learn a set of co-occurrence patterns of shapes from texture images, then establish a bag-of-words model on the learned shape co-occurrence patterns (SCOPs), and finally use the resulting SCOPs distributions as features for texture analysis. In contrast with existing work, the proposed method not only inherits the strong ability to depict geometrical aspects of textures and the high robustness to variations of imaging conditions from the shape-based texture analysis method, but also provides a more flexible way to model shape relationships (high-order statistics) on the tree. To our knowledge, this is the first time to use co-occurrence patterns of explicit shapes as a tool for texture analysis. Experiments of texture retrieval and classification on various databases report state-of-the-art results and demonstrate the efficiency of the proposed method.
引用
收藏
页码:1627 / 1632
页数:6
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