School of Information Science and Technology Northwest University, Xian, China

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Abstract—Weighted coefficients of image retrieval algorithm based on relevance feedback are determined in advance, which is lack of flexibility. In order to obtain satisfactory retrieval results, this algorithm requires a large amount of feedback calculation and efficiency of the algorithm is low. Aiming at the faults of relevance feedback, the adaptive adjustment algorithm of weighted coefficients based on quantum particle swarm optimization is presented, which is composed of user feedback process and particle evolution process. The particle encoding process and fitness function calculation process are worked out. The result of experiment using the Corel standard library, shows that quantum particle swarm optimization algorithm greatly improves the retrieval accuracy than the other image retrieval algorithms.

Index Terms—Relevance feedback, quantum particle swarm optimization, detection accuracy, weight adjustment.

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Cite: Shu-qin Liu and Jin-ye Peng, "A Novel Image Retrieval Algorithm Based on Adaptive Weight Adjustment and Relevance Feedback," Journal of Computers vol. 9, no. 11, pp. 2720-2726, 2014.