Further studies are required to understand the performance evaluation scores as we change the threshold function. This research answers how the ABC algorithm can be used in a sparse-land setup to solve video summarization. The evaluation scores are calculated on the VSUMM dataset for all the 50 videos against the two user summaries. For a list of valid name-value pair arguments, see the documentation for this function. but when i use it i get an error, 'MinPeakWidth' is not a recognized parameter.
![hist matlab 2014a hist matlab 2014a](https://demo.vdocument.in/img/378x509/reader019/reader/2020040505/5aededd07f8b9a6625909e21/r-2.jpg)
I need to use the findpeaks from Signal Processing Toolbox (SPT) as stated here. The results are compared to the existing methods. MATLAB: Findpeaks from signal processing toolbox does not work. We have used Matlab 2014a for doing the feature extraction and ABC algorithm for VS. In this work, we have used Image histogram, HOG, HOOF, and Canny edge features as features to the ABC algorithm.
![hist matlab 2014a hist matlab 2014a](https://img.dalfak.com/92/923068739-2120787801.jpg)
The frames with higher threshold errors form the video summarization. A simple threshold function is tested to see the reconstruction error of the current frame given the previous 50 frames from the dictionary.
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Define the bin edges with a vector, where the first element is the left edge of the first bin, and the last element is the right edge of the last bin. By doing video summarization in a quasi (real-time), we can solve other challenges like anomaly detection and Online Video Highlighting. Distribute 1,000 random numbers into bins. The critical challenge in doing quasi(real-time) video summarization is still time-consuming with ANN-based methods, as these methods require training time. Abstract: 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.