Image Retrieval with Relevance Feedback based on Genetic Programming

This paper presents a new content-based image retrieval framework with relevance feedback. This framework employs Genetic Programming to discover a combination of descriptors that better characterizes the user perception of image similarity. Several experiments were conducted to validate the proposed framework. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed framework was compared with three other relevance feedback methods regarding their efficiency and effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

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