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Color and Texture Based Image Retrieval

Author(s): Sridhar | Gowri

Journal: ARPN Journal of Systems and Software
ISSN 2222-9833

Volume: 2;
Issue: 1;
Start page: 1;
Date: 2012;
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Keywords: Image Recommendation | similarity-preserving Image retrieval | CBIR

Due to the repaid development of internet technology, image documents have become an important information source. It is hard to retrieve certain images from all available ones. An interactive image recommendation system, which firstly uses colorhistogram feature and GCLM texture feature to express image contents, then a kernel based K-means is utilized to cluster images into multiple classes by their visual features, finally based on a feature vectors stored in the database the similar images are retrieved. The HSV color histogram is calculated and the joint histogram is derived based on the combination of the hue and saturation in the hue and saturation histogram. The color feature is extracted from the joint histogram. The chi-square is used to find the similarity between the two images. Thus global feature is calculated using the joint histogram. The regional feature is extracted using the GCLM technique in which the neighbor pixels is considered into account. The evaluation resultsdemonstrate the accuracy of the retrieval based on the precision and recall false positive and negative ratio. The ROC curve is used to compare the efficiency of the color, texture and the combination of both the color and the texture.
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