期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：10
DOI：10.14569/IJACSA.2020.0111073
出版社：Science and Information Society (SAI)
摘要：This paper attempts to prove that the Artificial Bee Colony algorithm can be used as an optimization algorithm in sparse-land setup to solve Video Summarization. The critical challenge in doing quasi(real-time) video summarization is still time-consuming with ANN-based methods, as these methods require training time. By doing video summarization in a quasi (real-time), we can solve other challenges like anomaly detection and Online Video Highlighting. A simple threshold function is tested to see the reconstruction error of the current frame given the previous 50 frames from the dictionary. The frames with higher threshold errors form the video summarization. In this work, we have used Image histogram, HOG, HOOF, and Canny edge features as features to the ABC algorithm. We have used Matlab 2014a for doing the feature extraction and ABC algorithm for VS. The results are compared to the existing methods. The evaluation scores are calculated on the VSUMM dataset for all the 50 videos against the two user summaries. This research answers how the ABC algorithm can be used in a sparse-land setup to solve video summarization. Further studies are required to understand the performance evaluation scores as we change the threshold function.
关键词：Artificial Bee Colony optimization; video summarization; online video highlighting; sparse-land; anomaly detection; image histogram; HOG; HOOF; canny edge
