Relevance feedback and region based image retrieval are two effective ways to improve accuracy in content-based image retrieval. In this paper, we propose a content-based image retrieval method using relevance feedback and homogeneous region. By extracting a number of homogeneous color regions from the image and calculating the occurrence frequency of regions, we convert image feature vectors to weighted vectors. On the basis of the weighted vectors, we calculate the similarity between two weighted vectors and using relevant feedback technique. Our experimental results on a Wang database of over 10,000 images suggest that the technique results in which is close to user's intention better than the CBsIR and CCH methods.  Index Terms-content based image retrieval, weighted vectors, feature vectors, machine learning.

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In this paper we first analysis the Harbin method used for retrieving landscape images. Then we propose an improvement method based on the Harbin method that only compares each blocks of the image with some appropriate blocks in another image. This method decreases the retrieval time and to increase the retrieval accuracy of the Harbin method.We conducted an experimental study on an image database consisting of 525 landscape images. Our results show the degree of precision and speed of the proposed algorithm that is better than Harbin method.

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