Static video summarization approach using Binary Robust Invariant Scalable Keypoints

The constant demand and generation of digital
video information have recently resulted in an increase in the
growth of digital video content. Due to the rapid browsing of large
amounts of data, content retrieval and indexing of video require
an effective and advanced analysis technique. For quickly
browsing, indexing, and accessing massive video archives, video
summarizing approaches have been proposed. This research
presents a new binary descriptor-based method for video
summarization. The proposed method extracts key points and
descriptors using a Binary Robust Invariant Scalable Key point
(BRISK). For matching the binary descriptors between two
successive frames, we employ a Brute-force method. And
keyframes are extracted from each shot as the middle frame.
Experiments were carried out using open video project data sets
containing videos of various genres. The Comparison of user
summaries (CUS) evaluation metric is used to assess the proposed
method by calculating the accuracy and error rates and
comparing it to other methods. As demonstrated by the
experimental results, the proposed method gives good results
when compared with other methods.