Modified hierarchical genetic algorithm for relevance feedback in image retrieval

Abstract

A modified hierarchical genetic algorithm (mHGA) is proposed for relevance feedback in image retrieval. In the underlying image similarity model, image similarity is expressed as a weighted aggregation of the corresponding region similarities, while each region similarity is expressed as a weighted aggregation of the corresponding feature similarities. Two distinguishing characteristics of the proposed relevance feedback method are: (1) unlike the existing relevance feedback methods, mHGA modifies both aggregation operators and weights, in order to adapt the similarity model to the user; and (2) unlike the ordinary genetic algorithm (GA), mHGA automatically switches between different combinations of the four adaptation targets (region aggregation operator, region weights, feature aggregation operators, and feature weights). The resulting image similarity function is: (1) more general than in case of the existing image similarity models; and (2) mathematically simpler (and thus computationally faster) than corresponding function adapted by ordinary GA. The proposed method is evaluated on five test databases, with around 2,500 images, covering 62 semantic categories. Compared with twelve of the representative image retrieval methods, including four based on relevance feedback, the proposed method brings in average between 6% and 36% increase in the retrieval precision. Results suggest that using mHGA to adapt both aggregation operators and weights is an effective approach to the relevance feedback in image retrieval.