Segmentation of CE videos by finding convex skin

Reviewing video from wireless capsule endoscopy is a tedious work that requires concentration for extended hours. Computer algorithms are sought to reduce the review time and large number of training examples, to reduce the generalization error of machine learning algorithms. Therefore efficient and scalable approaches are needed which can modify knowledge structures in an incremental fashion without having to revisit all previously processed data. This paper proposes a novel incremental learning method for SVMs. The intuition is that the samples within the skin are retained alone in training. When additional samples are provided, they will be used together with the skin of the convex hull constructed from previous dataset. This result in much less number of instances and are used in incremental steps of the training process. Experiments were performed to segment frames of capsule endoscopy videos into four sections of gastrointestinal tract.