结合流形学习和相关反馈技术的图像检索方法关键是结合低层可视化信息,从少量用户反馈信息中学习用户语义,以获得语义子空间流形.为获得更真实的语义子空间,文中在区分对待低层可视化和用户反馈信息的同时,基于低层可视化信息选择学习反馈信息中的类内和类间关系,提出一种选择关系嵌入算法应用于图像检索.该方法可保留更真实的语义流形结构,从而提高在低维空间中的检索精度.实验结果表明文中方法可将图像映射到更广范围的低维空间,在反馈迭代两次之后检索精度提高最高可达16.3％.%The key of image retrieval based on manifold learning and relevance feedback is to learn user semantics from a few feedbacks based on the low-level visual information, to get semantic subspace manifold. To get more real semantic subspace, the low-level visualization and the user feedback information are differentiated, and the relations of inter-class and intra-class are learned selectively from feedback information based on visualization feature. Then, a selected relation embedding algorithm for image retrieval is proposed. By this algorithm, more realistic semantic manifold structures are kept, and the retrieval precision in low-dimensional space is enhanced. The experimental results show that the proposed method mapps the image into a wider range of low-dimensional space, and improves the retrieval precision up to 16. 3% after two feedback iterations.
展开▼
