Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis

Conventional Wireless capsule endoscopy (WCE) video summary generation techniques apprehend an image by extracting hand crafted features, which are not essentially sufficient to encapsulate the semantic similarity of endoscopic images. Use of supervised methods for extraction of deep features from an image need an enormous amount of accurate labelled data for training process. To solve this, we use an unsupervised learning method to extract features using convolutional auto encoder. Furthermore, WCE images are classified into similar and dissimilar pairs using fixed threshold derived through large number of experiments. Finally, keyframe extraction method based on motion analysis is used to derive a structured summary of WCE video. Proposed method achieves an average F-measure of 91.1% with compression ratio of 83.12%. The results indicate that the proposed method is more efficient compared to existing WCE video summarization techniques.