A comprehensive study of automatic video summarization techniques

Gupta, Deeksha; Sharma, Akashdeep

doi:10.1007/s10462-023-10429-z

Cited by 2 publications

(2 citation statements)

References 326 publications

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“…Then a self-motivated scoring mechanism is utilized for finding the frame importance score. A comprehensive review of the various techniques used is discussed in (5) . The survey presents genre-wise datasets and technique wise methodologies in a broader way.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Video Summarization using Visual Attention Technique

Dhanushree,

Priya,

Aruna

et al. 2024

IJST

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Objectives: To develop an efficient Video Summarization technique that aims to utilize the saliency map for mimicking the human way of selecting the important events in the given video. Methods: This paper proposes Histogram based Weighted Fusion (HWF) algorithm that uses spatial and temporal saliency maps to act as guidance in creating the summary of the video. The spatial saliency score and temporal saliency score obtained from the corresponding saliency maps are fused using the proposed HWF algorithm to obtain the frame level importance score. It tries to depict the visual attention of the human brain when watching a particular video. Findings: The experimental results show that the proposed HWF algorithm performs better than the state-of-the-art methods. Novelty: The use of Histogram intersection and the incorporation of the exponential function as the weight for the combined feature enhance the summarization ability of the proposed model. Keywords: Video Summarization, Saliency Map, Histogram intersection, Contrast sensitivity function, Attention curves

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Section: Introductionmentioning

confidence: 99%

A Framework for Video Summarization using Visual Attention Technique

Dhanushree,

Priya,

Aruna

et al. 2024

IJST

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“…Choosing a frame plays an important role (Gupta & Sharma, 2023) so the main idea is to summarize the important sections of the frames. consider the video 'Teacher leaves home in the morning for college and returns home after taking lectures and leaves again for taking lectures and returns to home in the evening' as an example.…”

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confidence: 99%

Auto encoder with mode‐based learning for keyframe extraction in video summarization

Shambharkar,

Goel

2023

Expert Systems

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The exponential increase in video consumption has created new difficulties for browsing and navigating through video more effectively and efficiently. Researchers are interested in video summarization because it offers a brief but instructive video version that helps users and systems save time and effort when looking for and comprehending relevant content. Key frame extraction is a method of video summarization that only chooses the most important frames from a given video. In this article, a novel supervised learning method ‘TC‐CLSTM Auto Encoder with Mode‐based Learning’ using temporal and spatial features is proposed for automatically choosing keyframes or important sub‐shots from videos. The method was able to achieve an average F‐score of 84.35 on TVSum dataset. Extensive tests on benchmark data sets show that the suggested methodology outperforms state‐of‐the‐art methods.

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A comprehensive study of automatic video summarization techniques

Cited by 2 publications

References 326 publications

A Framework for Video Summarization using Visual Attention Technique

A Framework for Video Summarization using Visual Attention Technique

Auto encoder with mode‐based learning for keyframe extraction in video summarization

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