Handbook of pattern recognition & computer vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Image retrieval based on the texton co-occurrence matrix
Pattern Recognition
A novel T-CAD framework to support medical image analysis and reconstruction
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
| Hi-index | 0.00 |
Statistical approaches and structural approaches have been extensively investigated in texture analysis of content-based image retrieval whereas little work has integrated them. This paper puts forward an effective texture descriptor-compact texton co-occurrence matrix (CTCM), interweaving the universal local structural textons and statistical co-occurrence matrix seamlessly. First, the CTCM transforms the gray image into a texton index image quickly, utilizing our proposed compact local binary pattern, which generates universal and compact texton dictionary with linear computation complexity. Then the low dimensional texton co-occurrence matrix (TCM) is achieved conveniently. At last, the histogram of TCM is constructed, which represents both the implicit statistical characteristics of individual textons and the explicit statistical characteristics of spatial texton pairs. Experimental results on Brodatz demonstrate that the retrieval performance of CTCM is superior over that of state-of-the-art methods, including the Gabor, local binary pattern and gray-level co-occurrence matrix; furthermore, the CTCM with linear computation complexity is overwhelmingly faster than the Gabor.