A Review on Key Features and Novel Methods for Video Summarization
Vinsent Paramanantham1, S. Suresh Kumar2

1Vinsent Paramanantham, Faculty of Computing, Sathyabama University, Chennai (Tamil Nadu), India.
2Dr. S. Suresh Kumar, Principal, Swarnandhra College of Engineering and Technology, Narasapur (A.P), India.
Manuscript received on 22 July 2022 | Revised Manuscript received on 16 February 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 88-105 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijeat.F37370811622 | DOI: 10.35940/ijeat.F3737.0212323

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper, we discuss techniques, algorithms, evaluation methods used in online, offline, supervised, unsupervised, multi-video and clustering methods used for Video Summarization/Multi-view Video Summarization from various references. We have studied different techniques in the literature and described the features used for generating video summaries with evaluation methods, supervised, unsupervised, algorithms and the datasets used. We have covered the survey towards the new frontier of research in computational intelligence technique like ANN (Artificial Neural Network) and other evolutionary algorithms for VS using both supervised and unsupervised methods. We highlight on single, multi-video summarization with features like video, audio, and semantic embeddings considered for VS in the literature. A careful presentation is attempted to bring the performance comparison with Precision, Recall, F-Score, and manual methods to evaluate the VS. 
Keywords: Video Summarization, Multi-View Video Summarization, Online Offline Video Highlighting, Key Frames, Sparse Coding, Feature Extraction, Sparse Land, CNN, RNN, LSTM.
Scope of the Article: Artificial Neural Network