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

Authors:
Kingkarn Sookhanaphibarn;Chidchanok Lursinsap
Affiliations:
Advanced Virtual and Intelligent Computing Center (AVIC), Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;Advanced Virtual and Intelligent Computing Center (AVIC), Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
Venue:
Information Sciences: an International Journal
Year:
2006

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Abstract

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 256x256 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.