Content based image retrieval based on eye physiological structure and relevance feedback
Content Based Image Retrieval (CBIR) includes a set of methods for processing visual features of a query image to find similar images at an images database. As extracting features and determining similarity measure, are two main stages in retrieval systems. In this paper we have tried to give weight to image pixels and suggest an effective feature vector by use of eye physiological structure and annotation issue. Then modify feature components weight and optimize similarity measure by use of information of relative and irrelative images in each feedback. So by means of annotation issue in physiological structure of eye, pixels in center of image are more important and have more effective role in extracted features and also system accuracy will be increased for determining similar images, by means of optimizing similarity measure in each stage.
Experiments were done on Corel image database with 5000 images from 30 different groups. In these experiments, accuracy of suggested method was compared to three image retrieval methods based on l*a*b* color histogram, HSV, and fuzzy edge histogram. As the results suggest, high accuracy of proposed method in compare with other methods in this area.
Keywords: image retrieval, relevance feedback, optimizing similarity function, eye physiological structure
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