Texture Image Segmentation Based on Gaussian Mixture Models and Gray Level Co-occurrence Matrix

摘要：

A novel texture image segmentation method based on Gaussian mixture models (GMM) and gray level cooccurrence matrix (GLCM) and was proposed. The feature space was formed by eight statics generated by gray level co-occurrence matrix (GLCM) including mean, variance, angular second moment(ASM), entropy, inverse difference moment(IDM), contrast, homogeneity (HOM), correlation(COR). The parameters of Gaussian mixture models were estimated by expectation maximization (EM) algorithm. The experiment results show that the proposed method can get better segmentation results than paper|8 and effectively enhance the segmentation precision of texture image.

关键词： Gaussian mixture models expectation maximization algorithm texture image segmentation gray level co-occurrence matrix (GLCM)

作者: YU Jian

作者单位: College of Mathematics and Information Technology Hanshan Normal University Chaozhou, Guangdong 521041, China

会议类型: 国际会议

会议名称: Third International Symposium on Information Science and Engineering(第三届信息科学与工程国际会议 ISISE 2010)

会议地点: 上海

会议语种:英文

页码: 149-152

在线出版日期: 2010-12-24（万方平台首次上网日期，不代表论文的发表时间）