Bridging the semantic gap using ranking SVM for image retrieval
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Content based image retrieval from chest radiography databases
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Hi-index | 0.00 |
Relevance feedback (RF) has been an active research area in Content-based Image Retrieval (CBIR). RF intends to bridge the gap between the low-level image features and the high-level human visual perception by analyzing and employing the feedback information provided by the user. This gap becomes more evident and important in medical image retrieval due to the two distinct facts with regard to medical images: (1) subtle differences between images, even between pathological and non-pathological images; (2) subjective and different diagnosis even among experts. This paper describes a novel linear weight-updating approach for RF applying to spine x-ray image retrieval. The algorithm utilizes both positive and negative examples to gain feedback from the user. Experimental results show that the proposed approach can substantially improve the retrieval performance to better satisfy the individual userýs preferences.