Texture Classification Using Wavelets with a Cluster-Based Feature Extraction

摘要：

After several decades of research, the development of an effective feature extraction method for texture classification is still an ongoing effort. In this paper, we propose a novel approach for texture classification using a new cluster-based feature extraction method that divides the matrices of computed two-dimensional wavelet coefficients into clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors serve as input patterns to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to the performance obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

作者: Gang Yu Sagar V. Kamarthi

作者单位: Department of Mechanical Engineering and Automation Harbin Institute of Technology (HIT) Shenzhen Gr Mechanical and Industrial Engineering Northeastern University 360 Huntington Ave.334SN Boston,MA 021

会议类型: 国际会议

会议名称: The 2nd International Symposium on Systems and Control in Aeronautics and Astronautics(第二届航空航天系统与控制国际会议 ISSCAA 2008)

会议地点: 深圳

会议语种:英文

页码: 157-161

在线出版日期: 2008-12-10（万方平台首次上网日期，不代表论文的发表时间）