Rotation-invariant colour texture classification through multilayer CCR

被引:41
作者
Bianconi, Francesco [1 ]
Fernandez, Antonio [2 ]
Gonzalez, Elena [2 ]
Caride, Diego [2 ]
Calvino, Ana [2 ]
机构
[1] Univ Perugia, Dipartimento Ingn Ind, I-06125 Perugia, Italy
[2] Univ Vigo, Escuela Tecn Super Ingn Ind, Vigo 36310, Spain
关键词
Colour texture classification; Rotation invariance; CCR; MODELS; SYSTEM;
D O I
10.1016/j.patrec.2009.02.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Coordinated Clusters Representation (CCR) is a texture descriptor based oil the probability of occurrence of elementary binary patterns (texels) defined over a square window. The CCR was originally proposed for binary textures, and it was later extended to grayscale texture images through global image thresholding. The required global binarization is a critical point of the method, since this preprocessing stage can wipe out textural information. Another important drawback of the original CCR model is its sensitivity against rotation. In this paper we present a rotation-invariant CCR-based model for colour textures which yields a two-fold improvement over the grayscale CCR: first, the use of rotation-invariant texels makes the model insensitive against rotation: secondly, the new texture model benefits from colour information and does not need global thresholding. The basic idea of the method is to describe the textural and colour Content of an image by splitting the original colour image into a stack of binary images, each one representing a colour of a predefined palette. The binary layers are characterized by the probability Of Occurrence Of rotation-invariant texels, and the overall feature vector is obtained by concatenating the histograms computed for each layer. In order to quantitatively assess our approach, we performed experiments over two datasets of colour texture images using five different colour spaces. The obtained results show robust invariance against rotation and a marked increase in Classification accuracy With respect to grayscale versions of CCR and LBP. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:765 / 773
页数:9
相关论文
共 36 条
[1]  
Arvis V., 2004, Image Analysis & Stereology, V23, P63, DOI 10.5566/ias.v23.p63-72
[2]  
Bianconi F, 2007, LECT NOTES COMPUT SC, V4756, P231
[3]   ASSIST: automatic system for surface inspection and sorting of tiles [J].
Boukouvalas, C ;
De Natale, F ;
De Toni, G ;
Kittler, J ;
Marik, R ;
Mirmehdi, M ;
Petrou, M ;
Le Roy, P ;
Salgari, R ;
Vernazza, G .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1998, 82 (1-3) :179-188
[4]   Texture classification system using colour space fusion [J].
Chindaro, S ;
Sirlantzis, K ;
Deravi, F .
ELECTRONICS LETTERS, 2005, 41 (10) :589-590
[5]   Experiments in colour texture analysis [J].
Drimbarean, A ;
Whelan, PF .
PATTERN RECOGNITION LETTERS, 2001, 22 (10) :1161-1167
[6]   COLOR CONSTANT COLOR INDEXING [J].
FUNT, BV ;
FINLAYSON, GD .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1995, 17 (05) :522-529
[7]   Color calibration for a dermatological video camera system [J].
Grana, C ;
Pellacani, G ;
Seidenari, S ;
Cucchiara, R .
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, 2004, :798-801
[8]  
Hauta-Kasari M., 1996, Proceedings of the 13th International Conference on Pattern Recognition, P785, DOI 10.1109/ICPR.1996.546930
[9]  
Hiremath PS, 2006, INT J COMPUT SCI NET, V6, P124
[10]   Image indexing using color correlograms [J].
Huang, J ;
Kumar, SR ;
Mitra, M ;
Zhu, WJ ;
Zabih, R .
1997 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, 1997, :762-768