基于粒子群的图像检索相关反馈算法

Image Retrieval Relevance Feedback Algorithm Based on Particle Swarm Optimization

将粒子群优化算法的进化搜索过程与用户的反馈过程有效结合,提出了一种基于粒子群的图像检索相关反馈算法,避免了初始检索对用户认知的影响以及对反馈效果造成的局限性,并使得用户对检索目标的理解逐渐清晰,能够有效全面的搜索图片库,同时避免多次反馈造成的算法效率和检索效果之间的矛盾.通过实验验证了算法的有效性.

The evolution searching process of particle swarm optimization（PSO） algorithm and user feedback process are combined effectively,and an image retrieval relevance feedback（RF） algorithm based on PSO is proposed,in which the influence to user perception and limitation to feedback effect caused by initial retrieval are avoided.The algorithm makes users′understanding to the retrieval goal become clear gradually,could search image database effectively and comprehensively,and at the same time it could avoid the contradiction of efficiency and retrieval effect caused by multi-feedback.Experiments have verified the validity of the proposed algorithm.