Search Result Clustering Based Relevance Feedback for Web Image Retrival

Although relevance feedback (RF) has been extensively studied in the information retrieval community, no commercial Web image search engines support RF because of usability, scalability, and efficiency issues. In this paper, we proposed a search result clustering (SRC) -based RF mechanism for Web image retrieval. The proposed SRC-based RF mechanism employs an effective search result clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images. Given the textual space, we could integrate RF into Web image retrieval in a practical way. The proposed mechanism shows advantage over traditional relevance feedback methods in the following two aspects. On the one hand, our relevance feedback scheme could catch and reflect user´s search intension precisely, for the noisy terms would be exempted from the term list with the aid of clustering, thus, the usability of RF in textual space for Web image retrieval is guaranteed. On the other hand, with the exemption of noisy term, the computation with regards to the low-dimensioned textual space is feasible; therefore, the issues of scalability and efficiency for Web image retrieval are addressed. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.