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• W2183655188 abstract "We are concerned with the extraction of effective color features for a contentbased image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods: Limited Rank Matrix Learning Vector Quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use supervised training data and provide a discriminant linear transformation of the original features to a lower-dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different, standard color spaces. We achieved significant improvement in every examined color space. The best results were obtained with features extracted from original features in the color spaces YCrCb, CIE" @default.
• W2183655188 created "2016-06-24" @default.
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• W2183655188 date "2009-01-01" @default.
• W2183655188 modified "2023-09-26" @default.
• W2183655188 title "Effective Color Features for Content Based Image Retrieval in Dermatology" @default.
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• W2183655188 hasPublicationYear "2009" @default.
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