A flower image retrieval method based on memetic feature selection algorithm

Contend-based flower image retrieval is one of the hottest and most challenging problem in content-based image retrieval area. In this paper, a flower image retrieval method is proposed based on a memetic algorithm. The proposed method, which combines a global search strategy with a local search strategy, uses a memetic algorithm to select the optimal feature subset. Genetic algorithm is used as the global search strategy while approximate Markov blanket is adopted as the local search strategy. Primary classifiers are trained using the proposed memetic algorithm for each kind of features. The probabilities obtained by all the primary classifiers are combined together to form a mid-level feature used to train the final classifier. Experimental results show that the proposed method selects fewer number of features with better precisions and recall ratios. That also brings improvements on retrieval time.