Adaptive Metrics for Content Based Image Retrieval in Dermatology

Apr, 2009

We apply distance based classifiers in the context of a content based image retrieval task in dermatology. In the present project, only RGB color information is used. We employ two different methods in order to obtain a discriminative distance measure for classification and retrieval: Generalized Matrix LVQ and Large Margin Nearest Neighbor approach. Both methods provide a linear transformation of the original features to lower dimensions. We demonstrate that both methods lead to very similar discriminative transformations and improve the classification and retrieval performances significantly

Link

@INPROCEEDINGS{Bunte_ESANN2009b,
author = {Kerstin Bunte and Michael Biehl and Nicolai Petkov and Marcel~F. Jonkman},
title = {{Adaptive Metrics for Content Based Image Retrieval in Dermatology}},
booktitle = {Proc. of the  17th "European Symposium on Artificial Neural Networks (ESANN)},
month = {"Apr."},
pages = {129--134},
editor = {M. Verleysen},
publisher = {D-facto Publications},
address = {Bruges, Belgium},
year = {2009},
url = {http://hdl.handle.net/11370/1c9d49a6-fee7-4c6b-b23c-0b31ad445e46},
url2 = {http://www.cs.rug.nl/~biehl/Preprints/cbiresann09.pdf},
abstract = {We apply distance based classifiers in the context of a content based image retrieval task in dermatology.  In the present project, only RGB color information is used. We employ two different methods in order to obtain a discriminative distance measure for classification and retrieval: Generalized Matrix LVQ and Large Margin Nearest Neighbor approach. Both methods provide a linear transformation of the original features to lower dimensions.  We demonstrate that both methods lead to very similar discriminative transformations and improve the classification and retrieval performances significantly},
}