Learning effective color features for content based image retrieval in dermatology

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

54 Citations (Scopus)
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Abstract

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.
Original languageEnglish
Pages (from-to)1892-1902
Number of pages11
JournalPattern recognition
Volume44
Issue number9
DOIs
Publication statusPublished - Sep-2011
Event13th International Conference on Computer Analysis of Images and Patterns - , Germany
Duration: 2-Sep-20094-Sep-2009

Keywords

  • Machine learning
  • Learning vector quantization
  • Adaptive distance measures
  • Content based image retrieval
  • PIGMENTED SKIN-LESIONS
  • MEDICAL APPLICATIONS
  • VECTOR QUANTIZATION
  • DIAGNOSIS
  • ALGORITHM

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