Fast Video Search on Recurring Segments

In this paper, we present a novel visual content search approach that can query quite fast, have low memory need and achieve successful results particularly for the instances with minor content changes. For this purpose, first, the content of a video is represented with sparsely sampled edge energy variations among video frames. Then, these high dimensional features are converted into simple signatures using an approach presented in [1]. During a query, these signatures are compared with a novel data structure model which needs low memory burden, and thus similar videos and their overlapping time intervals are estimated. 11 commercial videos downloaded from YouTube are utilized for the test. Furthermore, these videos are augmented with different compression parameters. Reference video archive consists of 11 new videos composed of the original query videos in different time orders and additional 150 hours video dataset that contains none of the query videos. The results validate that the proposed method is fully effective in computation speed and memory requirements.