k-NN、SVM和NN在共现直方图放大内镜结肠图像坑模式分类中的比较

M. Häfner, Alfred Gangl, F. Wrba, K. Thonhauser, Haiko Schmidt, C. Kastinger, A. Uhl, A. Vécsei
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引用次数: 12

摘要

以共现直方图为特征,利用k-NN、SVM和NN分类器对放大内窥镜图像进行分类。在k-NN分类情况下,这些直方图可以提高简单ID颜色直方图的分类精度,在2类情况下可提高10%,在6类情况下可提高5%。支持向量机和神经网络分类器的分类结果是非竞争的,并且不能改善ID颜色直方图的分类结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of k-NN, SVM, and NN in Pit Pattern Classification of Zoom-Endoscopic Colon Images using Co-Occurrence Histograms
Co-occurrence histograms are used as features to classify magnifying endoscope imagery with k-NN, SVM, and NN classifiers. In the k-NN classification case these histograms may improve the classification accuracy of simple ID color histograms up to 10% in the 2 classes case and up to 5% in the 6 classes case. The classification results of SVM and NN classifiers have turned out to be noncompetitive and do not improve the classification result of ID color histograms.
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