A new feature extractor invariant to intensity, rotation, and scaling of color images

作     者：Sookhanaphibarn, Kingkarn Lursinsap, Chidchanok 

作者机构：Chulalongkorn Univ Fac Sci Dept Math Adv Virtual & Intelligent Comp Ctr Bangkok 10330 Thailand 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2006年第176卷第14期

页      面：2097-2119页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：translation scaling and rotation transformation intensity invariance invariant object recognition (IOR) colored-texture images competitive learning algorithm principal component analysis (PCA) rotational direction algorithm 

摘      要：This paper proposes a new method for extracting the invariant features of an image based on the concept of principal component analysis and a competitive learning algorithm. The proposed algorithm can be applied to binary, gray-level, or colored-texture images with a size greater than 256 x 256 pixels. In addition to translation, scaling, and rotation invariant extraction, the extraction of a feature invariant to color intensity can be implemented by using this method. In our experiment, the proposed method shows the capability to differentiate images having the same shape but different colored textures. The experimental results report the effectiveness of this technique and its performance as measured by recognition accuracy rate and computational time. These results are also compared with those obtained by classical techniques. (C) 2005 Elsevier Inc. All rights reserved.