Unsupervised Color Texture Segmentation Research Articles

Unsupervised color texture segmentation based on multi-scale region-level Markov random field models

In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors.

Unsupervised color–texture segmentation based on multiscale quaternion Gabor filters and splitting strategy

This paper proposes a new method for color–texture segmentation based on a splitting framework with graph cut technique. To process the scale difference of quaternion Gabor filter (QGF) features of a color textured image, a new multiscale QGF (MQGF) is introduced to describe texture attributes of the given image. Then, the segmentation is formulated in terms of energy minimization gradually obtained using binary graph cuts, where color and MQGF features are modeled with a multivariate finite mixture model, and minimum description length (MDL) principle is integrated into this framework as a splitting criterion. In contrast to previous approaches, our method finds an optimal segmentation by balancing energy cost and coding length, and the segmentation result is determined during the splitting process automatically. Experimental results on both synthetic and real natural color textured images demonstrate the good performance of the proposed method.

Unsupervised color–texture segmentation based on soft criterion with adaptive mean-shift clustering

An improved approach for J value segmentation (JSEG) is presented for unsupervised color–texture segmentation. Instead of the color quantization algorithm used in JSEG, an automatic classification method using adaptive mean-shift (AMS) clustering is applied for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the improved method overcomes the limitations of JSEG successfully and is more robust.