A New Method Based on Fused Features and Fusion of Multiple Classifiers Applied to Texture Segmentation

摘要：

Texture image segmentation consists of two stages: feature extraction and classification. The new method advanced in this paper fuses the Log-Gabor filter and DCT features in the first stage, then uses the fusion of Fuzzy c-Means (FCM) and Support Vector Machines (SVM) classifier to cluster the fused feature sets. The fused feature sets produce higher feature space separations, and the fusion of multi-classifiers performs the better clustering effect. The new method is demonstrated to produce higher segmentation accuracies relative to the individual feature and individual classifier, as well as outperform individual feature for noisy images with different noise magnitudes. The fused features and classifier fusion are advocated as means for improving texture segmentation performance.

作者: Yi LI Yingle FAN Jian XIANG

作者单位: Hangzhou Dianzi University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）