Visual Attention Based Keyframes Extraction and Video Summarization

Geetha, P.; Pandeeswari, S. Thiruchadai

doi:10.5121/csit.2012.2219

Cited by 5 publications

(1 citation statement)

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“…Human attention mechanism has been simulated to produce semantic video summary based on keyframe extraction. Visual attention of each frame is quantified using a descriptor named attention quantifier, which indicates color conspicuousness and the motion with more attention involved [26]. There have been many attempts to analyze visual content of video for keyframe extraction for video partitioning and summarization [27,28].…”

Section: Related Workmentioning

confidence: 99%

A novel keyframe extraction method for video classification using deep neural networks

Kızıltepe

Gan

Escobar

2021

Neural Comput & Applic

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Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy.

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Section: Related Workmentioning

confidence: 99%