| 저자 | 윤태복, 최영미, 주문원 |
|---|---|
| 학회명 | 한국 정보처리학회 추계학술대회 |
| 학회명 (약자) | |
| 페이지 | 705-708 |
| 학회시작일 | 2010-11-12 |
| 학회종료일 | 2010-11-13 |
An eigen color co-occurrence approach is proposed that exploits the correlation between color channels to identify the degree of image similarity. This method is based on traditional co-occurrence matrix method and histogram equalization. On the purpose of feature extraction, eigen color co-occurrence matrices are computed for extracting the statistical relationships embedded in color images by applying Principal Component Analysis (PCA) on a set of color co-occurrence matrices, which are computed on the histogram equalized images. That eigen space is created with a set of orthogonal axes to gain the essential structures of color co-occurrence matrices, which is used to identify the degree of similarity to classify an input image to be tested for various purposes. In this paper RGB, Gaussian color space are compared with grayscale image in terms of PCA eigen features embedded in histogram equalized co-occurrence features. The experimental results are presented.