Video-Specific SVMs for Colonoscopy Image Classification
نویسندگان
چکیده
We propose a novel classification framework called the videospecific SVM (V-SVM) for normal-vs-abnormal white-light colonoscopy image classification. V-SVM is an ensemble of linear SVMs, with each trained to separate the abnormal images in a particular video from all the normal images in all the videos. Since V-SVM is designed to capture lesion-specific properties as well as intra-class variations it is expected to perform better than SVM. Experiments on a colonoscopy image dataset with about 10, 000 images show that V-SVM significantly improves the performance over SVM and other baseline classifiers.
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