Endoscopy video summarization based on unsupervised learning and feature discrimination

We propose a novel endoscopy video summarization approach based on unsupervised learning and feature discrimination. The proposed learning approach partitions the collection of video frames into homogeneous categories based on their visual and temporal descriptors. Also, it generates possibilistic memberships in order to represent the degree of typicality of each video frame within every category, and reduce the influence of noise frames on the learning process. The algorithm learns iteratively the optimal relevance weight for each feature subset within each cluster. Moreover, it finds the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. The endoscopy video summary consists of the most typical frames in all clusters after discarding noise frames. We compare the performance of the proposed algorithm with state-of-the-art learning approaches. We show that the possibilistic approach is more robust. The endoscopy videos collection includes more than 90k video frames.