Learning metrics for content-based medical image retrieval
Application of content-based image retrieval (CBIR) to medical image analysis has recently become an active research field. While many previous studies have focused on the feature design, the metric design, another key CBIR component, has not been well investigated in this application context. This paper presents a medical CBIR that adapts its similaritymetric from data by using information theoretic metric learning. Also we systematically compare our SIFT bag-of-words-based system with various plug-in similarity measures available in literature. The proposed systems are evaluated with the ImageCLEF-2011 benchmarking dataset. Our experimental results demonstrate the advantage of the proposed metric learning approach and L1 distance-based measures.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.