Image Features Optimizing for Content-Based Image Retrieval

摘要：

Developing low-dimensional semantics-sensitive features is crucial for content-based image retrieval (CBIR). In this paper, we present a method called M2CLDA (merging 2-class linear discriminant analysis) to capture low-dimensional optimal discriminative features in the projection space. M2CLDA calculates discriminant vectors with respect to each class in the one-vs-all classification scenario and then merges all the discriminant vectors to form a projection matrix. The dimensionality of the M2CLDA space fits in with the number of classes involved. Moreover, when a new class is added, the new M2CLDA space can be approximated by only calculating a new discriminant vector for the new class. The features in the M2CLDA space have better semantic discrimination than those in traditional LDA space. Our experiments show that the proposed approach improves the performance of image retrieval and image classification dramatically.

关键词： 2-class LDA image retrieval image semantics discriminative features

作者: Zhiping Shi Xi Liu Qing He Zhongzhi Shi

作者单位: Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences Beijing,China,100190

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 2789-2793

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）