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Efficient SVM-based approach for CBIR

The fundamental aim of the research presented in the paper is to evolve general purpose clustering method that can efficiently handle large capacity image databases. In this paper a novel algorithm based on combination of K-means clustering and SVM was developed to improve standard general-purpose approach on clustering and to provide relevant results during retrieval process. Introduced technique of choosing preliminary number of clusters makes better use of K-means approach, while SVM-based technique improves clustering by correcting excessive generalization, that usually happens due to local optima problem. The experimental results show that the proposed method reaches high recall and precision.