A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback

In this paper, a sub-vector weighting scheme is proposed for the case of small sample in image retrieval with relevance feedback. By partitioning a multi-dimensional visual feature vector to multiple sub-vectors, the singularity problem caused by small sample can be avoided by the lower dimensionality of the sub-vectors. Then the optimal weighting can be performed on these sub-vectors respectively and the similarity scores obtained are combined as the final score to rank the database images. Experimental results demonstrated that the proposed weighting scheme can significantly improve the efficacy of image retrieval with relevance feedback.

• Type: Journal
• Year: 2002
• Where: IJIG
• Authors: Lei Wang, Kap Luk Chan, Xuejian Xiong
• Keywords: IJIG 2002, Relevance Feedback, Small Sample, Weighting Scheme