Efficient Relevance Feedback Using Semi-supervised Kernel-specified K-means Clustering
In this paper, we present an efficient and convenient relevance feedback (RF) by using a semi-supervised kernel-specified k-means clustering (SKKC) technique. SKKC is used to cluster the retrieval results so that RF can be conducted on the cluster level. Compared with traditional RF conducted on the point/single-image level, the new RF will facilitate the RF selection and reduce user's efforts on it. Furthermore, the proposed approach enables an accumulated learning ability by recording and learning from the history of users' RFs. The new RF is applied in a content-based medical image retrieval (CBMIR) system. Experimental results on ImageCLEF database of around 9,000 images have shown that the proposed new RF is able to improve effectiveness and efficiency of CBMIR