多点多样性密度算法及其在图像检索中的应用  

Multi-points diverse density learning algorithm and its application in image retrieval

作　　者：陈绵书[1] 杨树媛[1] 赵志杰[2] 付平[1] 孙元[3] 李晓妮[1] 孙言[1] 齐小隐[1] 

机构地区：[1]吉林大学通信工程学院,长春130022 [2]吉林大学仪器科学与电气工程学院,长春130022 [3]吉林大学公共计算机教学与研究中心,长春130022

出　　处：《吉林大学学报（工学版）》2011年第5期1456-1460,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(60832002);国家自然科学基金国际合作项目(609111301281);吉林大学科学前沿与交叉学科创新项目(200903297);吉林大学研究生创新基金项目(20111061);吉林省自然科学基金项目(20101515);吉林省科技发展计划项目(20090302;20090113)

摘　　要：提出多点多样性密度(MPDD)算法。针对经典多样性密度(DD)算法对样本包内容表述单一问题,采用多个密度点对包内容进行描述,避免了内容表述的片面性。将MPDD算法应用于图像检索。图像被均匀细分成小块,提取图像的颜色特征和纹理特征表示图像块内容,然后采用K均值算法对实现图像块进行聚类,产生包示例,最后应用MPDD算法对图像进行检索。实验结果表明,MPDD算法的检索效果好于DD算法。A Multi-Points Diverse Density（MPDD） learning algorithm was proposed.In classical diverse density learning algorithm,only one point was used to represent the content of bags,which was not enough.In the proposed MPDD learning algorithm,multi-points were utilized to contain more information.This algorithm was applied to image retrieval.First,an image was divided into small blocks,and the color and texture features of these small blocks were extracted to describe the content of an image block.Then,K-means algorithm was used to cluster image blocks to get instances.Finally,the MPDD learning algorithm was employed to retrieval images.Experiment results show that the performance of the proposed MPDD algorithm is superior to classical diverse density learning algorithm.

关 键 词：信息处理技术 多示例学习 多点多样性密度 多样性密度 图像检索 

分 类 号：TN911.73[电子电信—通信与信息系统]