Abstract
Wireless capsule endoscopy (WCE) is medical examination process for gastrointestinal tract (GIT). This noninvasive multi advantageous procedure can be made more popular by overcoming the problem of prolonged analysis time. Video summarization is a concise and meaningful representation of a video. Along with automated detection and segmentation methods, summarized video will serve as an additional source for confirming the analysis of WCE video before the final diagnosis without any dropouts. Nowadays, Internet of Things (IoT) environments are predominant in healthcare sectors. Considering limited resources of smart phones and long duration of WCE process, it is impractical to send all the WCE data to health-care centers or gastroenterologists. This paper reviews video summarization types and the techniques used for WCE video summarization by various researchers. Feature set selection, clustering methods and key frame selection techniques play important role in performance of summarization technique.
Highlights
Wireless capsule endoscopy (WCE) is medical examination process for gastrointestinal tract (GIT)
The WCE process involves swallowing of a camera loaded in a capsule which takes images of GIT during its movement through the entire tract
Video summarization technique based on possibilistic clustering and feature weighting algorithm is proposed in [8] by Mohamed et al The algorithm generates possibilistic membership which is used to detect the degree of similarity the WCE video frames
Summary
These are the two broad categories of summarization techniques. The static type summarized video contains set of key frames from the base video without consideration of time and sequence. The dynamic type summarized video contains the most significant, small portions of audio and video. They are called as video skims and are similar to trailers of movies. In the applications where quick response is needed and the scenes of video are not complex low level features does the work e.g. color, texture, motion etc. In the applications where contents of the image are of critical importance, high level features contribute more in the task of summarization. Multiple features investigate the content from various aspects and utilize them for video summarization. The evaluation of summarization technique with multiple features is necessary considering the area of application, practicability and its cost efficiency
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