A Comparative Analysis of Video Summarization Techniques

A Comparative Analysis of Video Summarization Techniques

Автор: Darshankumar D.Billur, Manu T.M., Vishwas Patil

Журнал: International Journal of Engineering and Manufacturing @ijem

Статья в выпуске: 3 vol.13, 2023 года.

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Video summarization special field of signal processing which includes pre-processing of video sets, their contextual segmentation, application-specific feature extraction & selection, and identification of dissimilar frame sets. Various variety of machine learning models are proposed by researchers to design such summarization methods, and each of them varies in terms of their functional nuances, application-specific advantages, deployment specific limitations, and contextual future scopes. Moreover, these models also vary in terms of quantitative & qualitative measures including accuracy of summarization, computational complexity, delay needed for summarization, precision during the summarization process, etc. Due to such a wide variation in performance levels, it is difficult for researchers to identify optimal models for their functional-specific &performance-specific use cases. Because of this, researchers and summarization-system-designers are required to validate individual models, which increases the delay & cost needed for final model deployments. To overcome these delays & reduce deployment costs, this paper initially discusses a multiple variety of video summarization models in terms of their working characteristics. Based on this discussion, researchers shall be able to identify optimum models for their functionality-specific use cases. This paper also analyzes and compares the reviewed models in terms of their performance metrics including summarization accuracy, delay, complexity, scalability and fMeasure, which will further allow readers to identify performance-specific models for their deployments. A novel Summarization Rank Metric (SRM) is calculated based on these evaluation metrics, which will assist readers to identify models that can perform optimally w.r.t. multiple evaluation parameters & different use cases. This metric is calculated by combining all the comparison metrics, which will assist in identification of models that have high accuracy, low delay, low complexity, high scalability & fMeasure levels.

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Video, Summarization, Deep Learning, Bioinspired, SRM, Accuracy, Delay, Complexity, Scalability

Короткий адрес: https://sciup.org/15018695

IDR: 15018695   |   DOI: 10.5815/ijem.2023.03.02

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