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

Authors:
Yuzhong Wang;Jie Yang;Ningsong Peng
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, P.O. Box 104, No. 1954 Hua Shan Road, Shanghai 200030, PR China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, P.O. Box 104, No. 1954 Hua Shan Road, Shanghai 200030, PR China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, P.O. Box 104, No. 1954 Hua Shan Road, Shanghai 200030, PR China
Venue:
Pattern Recognition Letters
Year:
2006

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Abstract

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.