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Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion
2008
Computerized Medical Imaging and Graphics
We present a content-based image retrieval framework for diverse collections of medical images of different modalities, anatomical regions, acquisition views, and biological systems. For the image representation, the probabilistic output from multi-class support vector machines (SVMs) with low-level features as inputs are represented as a vector of confidence or membership scores of pre-defined image categories. The outputs are combined for feature-level fusion and retrieval based on the
doi:10.1016/j.compmedimag.2007.10.001
pmid:18037271
fatcat:legkecovjzhmdhpbuvqkaym7oi