Improving image retrieval efficiency using a fuzzy inference model and genetic algorithm

Abstract

The typical approaches for content-based image retrieval extract signatures such as color, shape, and texture from each image and map the features into a d-dimensional metric space. According to the similarity measurement, the images closer to the signatures are shown to the users and constitute the query results. Such a retrieval method is prohibited in matching similar images from global similarity measurement that fails to consider partially matched images. In this paper, a genetic algorithm-based approach is proposed for the retrieval of images which may have been rotated and transposed. The approach utiflizes fuzzy inference model to segment an image into some regions according to imagery contents. Then, the color histograms are calculated from each region to reflect content feature property. After users submit a query image, the system can generate a population pool as the potential solutions from possible combinations of query image regions. The system applies the genetic operations and maintains the best top 5 images. Rotation and position displacement of objects are taken into account. Unlike retrieval based on individual region-matching, the proposed scheme performs an overall similarity computation. A thorough experiment demonstrates the robustness of the proposed system.