Using Local Latent Semantic Indexing with Pseudo Relevance Feedback in Web Image Retrieval

Latent Semantic Indexing (LSI) and Relevance Feedback (RF) have been shown to be extremely useful in information retrieval respectively. But at the context of Web image retrieval, LSI is limited for the Single Value Decomposition (SVD) computation cost to large dataset while RF is confused with the reluctance of interaction for most Web users. In this paper, a Pseudo Relevance Feedback approach based on Local Latent Semantic Indexing (PRF-LLSI) is proposed, which integrating the LSI and RF, and making use of the benefit of them while solving the limitation of them. The Local LSI (LLSI) method performs a low-dimensional SVD on the local region of initial retrieved results. Both keywords and image contents of the Web images are computed by LLSI to re-rank the initial retrieval results automatically. The PRF-LLSI contribute to the following: (1) Local LSI resolves the heavy computation cost of LSI; (2) Pseudo Relevance Feedback doesn't need the user's interaction; (3) LLSI combine the textual and visual features, which improves the precision of the system. The experiments are done in our VAST (VisuAl & SemanTic image search) system, and the results show the effectiveness of the proposed method.