Learning effective color features for content based image retrieval in dermatology

Kerstin Bunte*, Michael Biehl, Marcel F. Jonkman, Nicolai Petkov

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

54 Citaten (Scopus)
386 Downloads (Pure)


We investigate the extraction of effective color features for a content-based 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 to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially 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 improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1–25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter κ of LMNN (κ≥25) while LiRaM LVQ outperforms LMNN for smaller values of κ. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images.
Originele taal-2English
Pagina's (van-tot)1892-1902
Aantal pagina's11
TijdschriftPattern recognition
Nummer van het tijdschrift9
StatusPublished - sep-2011
Evenement13th International Conference on Computer Analysis of Images and Patterns - , Germany
Duur: 2-sep-20094-sep-2009

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