Fuzzy Based Texton Binary Shape Matrix (FTBSM) for Texture Classification

Texton is a extensively applied approach for texture analysis. This technique shows a strong dependence on certain number of parameters. Unfortunately, each variation of values of any parameter may affect the texture characterization performance. Moreover, micro structure texton is unable to extract texture features which also have a negative effect on the classification task. This paper, deals with a new descriptor which avoids the drawbacks mentioned above. To address the above, the present paper derives a new descriptor called Fuzzy Based Texton Binary Shape Matrix (FTBSM) for clear variation of any feature/parameter. The proposed FTBSM are defined based on similarity of neighboring edges on a 3D7;3 neighborhood. With micro-structures serving as a bridge for extracting shape features and it effectively integrates color, texture and shape component information as a whole for texture classification. The proposed FTBSM algorithm exhibits low dimensionality. The proposed FTBSM method is tested on Vistex and Akarmarble texture datasets of natural images. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as logical operators and GLCM and LBP, for texture classification

Texture Analysis and Classification Based on Fuzzy Triangular Greylevel Pattern and Run-Length Features

Your Texture analysis is one of the most important techniques used in the analysis and interpretation of images, consisting of repetition or quasi repetition of some fundamental image elements. The present paper derived Fuzzy Triangular Greylevel Pattern (FTGP) to overcome the disadvantages of LBP and other local approaches. The FTGP is a 2 x 2 matrix that is derived from a 3 x 3 neighborhood matrix. The proposed FTGP scheme reduces the overall dimension of the image while preserving the significant attributes, primitives, and properties of the local texture. From each 3 x 3 matrix a Local Grey level Matrix (LGM) is formed by subtracting local neighborhoods by the gray value of its center. The 2 x 2 FTGP is generated from LGM by taking the average value of the Triangular Neighbor Pixels (TNP) of the 3 x 3 LGM. A fuzzy logic is applied to convert the Triangular Neighborhood Matrix (TNM) in to fuzzy patterns with 5 values {0, 1, 2, 3 and 4} instead of patterns of LBP which has two values {0, 1}. On these fuzzy patterns a set of Run Length features are evaluated for an efficient classification. The proposed method is experimented with wide variety of textures, and exhibited with a high classification rate. The proposed FTGP with run length features shown its supremacy and efficacy over the various existing methods in classification of textures

Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images