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One-against-one classification for zoom-endoscopy images

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6 Author(s)
Hafner, M. ; Dept. of Gastroenterology & Hepatology, Med. Univ. of Vienna, Vienna ; Kwitt, R. ; Wrba, F. ; Gangl, A.
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In this paper, we present a novel approach for the classification of zoom-endoscopy images based on the pit-pattern classification scheme. Our feature generation step is based on the computation of a set of statistical features in the wavelet-domain. In the classification step, we employ a one-against-one approach using 1-Nearest Neighbor classifiers together with sequential forward feature selection. Our experimental results show that this classification approach drastically increases leave-one-out crossvalidation accuracy for our six-class problem, compared to already existing approaches in this research area.

Published in:

Advances in Medical, Signal and Information Processing, 2008. MEDSIP 2008. 4th IET International Conference on

Date of Conference:

14-16 July 2008