Feature selection using genetic algorithm and cluster validation

Feature selection plays an important role in image retrieval systems. The better selection of features usually results in higher retrieval accuracy. This work tries to select the best feature set from a total of 78 low level image features, including regional, color, and textual features, using the genetic algorithms (GA). However, the GA is known to be slow to converge. In this work we propose two directions to improve the convergence time of the GA. First we employ the Taguchi method to reduce the number of necessary offspring to be tested in every generation in the GA. Second we propose to use an alternative measure, the Hubert’s Γ statistics, to evaluate the fitness of each offspring instead of evaluating the retrieval accuracy directly. The experiment results show that the proposed techniques improve the feature selection results by using the GA in both time and accuracy.