Relevance feedback for spine X-ray retrieval

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 user's preferences.

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