基于改进布谷鸟搜索算法的相关反馈图像检索  

Relevance Feedback Image Retrieval Based on Modified Cuckoo Search Algorithm

作　　者：符保龙[1] 张爱科[1] 

机构地区：[1]柳州职业技术学院,广西柳州545006

出　　处：《电视技术》2014年第3期39-42,共4页Video Engineering

基　　金：广西教育厅科研项目(201106LX745;201204LX593)

摘　　要：由于视觉低层特征与高层语义间存在"语义鸿沟",基于内容的检索算法难以找到满足用户要求的图像,为了提高图像检索准确率,提出一种基于布谷鸟搜索算法的相关反馈图像检索方法(MCS)。首先分别提取图像的颜色、纹理、形状特征。然后根据用户的反馈信息,采用布谷鸟搜索算法动态调整特征的权值,从而建立满足用户实际偏好的图像相似度模型。最后采用仿真实验测试MCS的有效性。结果表明,相对于遗传算法、粒子群算法以及传统图像检索算法,MCS算法不仅提高了图像检索准确度,同时加快了图像检索效率,更好地满足图像检索要求。Traditional retrieval algorithm is difficult to satisfy with the user＇ s requirements because of ＂semantic gap＂ between visual low-level features and high-level semantic,in order to improve the accuracy of the image retrieval ,a relevance feedback image retrieval method based on modified cuckoo search algorithm（MCS） is proposed in this paper. Firstly,the color and texture of image are extracted,and then the cuckoo search algorithm is used to dynamically adjust the feature weights according to the feedback information of users, and the image similarity model is built to meet user actual prefer- ences. Finally, the simulation experiments are carried out to test the performance of MCS. The results show that, compared with the genetic algorithm, particle swarm algorithm and traditional image retrieval algorithms, the proposed algorithm not only has improved the accuracy of image retrieval, and fastened the image retrieval speed, it can better meet the needs of image retrieval.

关 键 词：图像检索 相关反馈 特征权值 布谷鸟搜索算法 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]