Semantic Discriminative Projections for Image Retrieval
Subspace learning has attracted much attention in image retrieval.In this paper,we present a subspace learning approach called Semantic Discriminative Projections (SDP),which learns the semantic subspace through integrating the descriptive information and discriminative information.We first construct one graph to characterize the similarity of contented-based features,another to describe the semantic dissimilarity.Then we formulate constrained optimization problem with a penalized difference form.Therefore,we can avoid the singularity problem and get the optimal dinlensionality while learning a semantic subspace.Furthermore,SDP may be conducted in the original space or in the reproducing kernel Hilbert space into which images are mapped.This gives rise to kernel SDP.We investigate extensive experiments to verify the effectiveness of our approach.Experimental results show that our approach achieves better retrieval performance than state-of-art methods.
He-Ping Song Qun-Sheng Yang Yin-Wei Zhan
Faculty of Computer,GuangDong University of Technology 510006 Guangzhou,P.R.China
国际会议
4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)
哈尔滨
英文
34-43
2008-01-16（万方平台首次上网日期，不代表论文的发表时间）