Spatial frequency channels and perceptual grouping in texture segregation
Computer Vision, Graphics, and Image Processing - Special issue on human and machine vission, part II
Ten lectures on wavelets
Texturing and modeling: a procedural approach
Texturing and modeling: a procedural approach
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification Using Windowed Fourier Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Landscape Features for Texture Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Plant leaf identification using Gabor wavelets
International Journal of Imaging Systems and Technology
Texture analysis and classification using deterministic tourist walk
Pattern Recognition
Texture based segmentation using graph cut and Gabor filters
Pattern Recognition and Image Analysis
A simplified gravitational model for texture analysis
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
A simplified gravitational model to analyze texture roughness
Pattern Recognition
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
| Hi-index | 0.10 |
Texture is a very important attribute in the field of computer vision. This work proposes a novel texture analysis method which is based on graph theory. Basically, we convert the pixels of an image into vertices of an undirected weighted graph and explore the shortest paths between pairs of pixels in different scales and orientations of the image. This procedure is applied to Brodatz's textures and UIUC texture dataset in order to evaluate its capacity of discriminating different kinds of textures. The best classification results using the standard parameters of the method are 98.50%,67.30% and 88.00% of success rate (percentage of samples correctly classified) for Brodatz's textures, UIUC textures (image size of 200x200 pixels), and original UIUC textures (image size of 640x480 pixels), respectively. These results prove that the proposed approach is an efficient tool for texture analysis, once they are superior to the results achieved by traditional and novel texture descriptors presented in literature.