Retrieval of Digital Images Based On Multi-Feature Similarity Using Genetic Algorithm

Conventional relevance feedback schemes may not be suitable to all practical applications of content based image retrieval (CBIR), since most ordinary users like to complete their search in a single interaction, especially on web search. In this paper, we explore a new approach based on multifeature similarity score fusion using genetic algorithm. Single feature describes image content only from one point of view, which has a certain one-sided. Fusing multifeature similarity score is expected to improve the system's retrieval performance. In this paper, the retrieval results from color feature and texture feature are analyzed, and the method of fusing multi-feature similarity score is described. For the purpose of assigning the fusion weights of multi-feature similarity scores reasonably, the genetic algorithm is applied. For comparison, other three methods are implemented. They are image retrieval based on color feature, texture feature and fusion of color-texture feature similarity score with equal weights. The experimental results demonstrate the image retrieval performance of the proposed method is superior to other methods.