Interactive genetic algorithm with relevance feedback for content based image retrieval

The prime goal of the CBIR system is to construct meaningful descriptions of physical attributes from images. Physical features and mathematical features are two such typical descriptions. To extract physical features such as color, texture, edge, structure or a combination of two or more. The majority of the proposed solutions are variations of the color histogram initially proposed for object recognition. Since color histogram lacked spatial information methods liable to produce false positives especially when the database was large. We proposed a method called image retrieval using genetic algorithmic procedures for computing a very large number of highly selective features and comparing the features for some relevant images using only selected features can capture similarity in the given relevant images for image retrieval. This research present our review on benchmark image datasets, color spaces which are used for implementation of CBIR process, image content as color, texture and shape attributes, and feature extraction techniques, similarity measures, feature set formation and reduction techniques, image indexing applied in the process of retrieval along with various classifiers with their effect in retrieval process, effect of relevance feedback and its importance in retrieval.