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- W4383670017 abstract "Video summarization refers to creating a temporally abridged version of video containing all the important highlights required for context understanding without the loss of original information. Segmentation and feature extraction are the two pre-processing tasks to be carried out on the input video sequence in any summarization framework. Segmentation divides the video into non-intersecting temporal segments while feature extraction process represents entire video in the form of feature vectors. This paper investigates video feature extraction using pre-trained deep neural networks, viz., GoogleNet, ResNet, and ResNeXt. These deep networks are employed to extract feature vectors from the video frames and the extracted features are used to summarize videos using summarization models. The efficacy of these deep networks for feature extraction step is then compared in terms of F1-scores of summarized videos. Our experimentation revealed that the performance of a deep network for feature extraction depends on nature of videos and summarization approaches. It is observed that GoogleNet is optimum choice for feature extraction in video summarization application." @default.
- W4383670017 created "2023-07-09" @default.
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- W4383670017 date "2023-01-01" @default.
- W4383670017 modified "2023-10-14" @default.
- W4383670017 title "A Comparative Investigation of Deep Feature Extraction Techniques for Video Summarization" @default.
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- W4383670017 doi "https://doi.org/10.1007/978-981-99-0483-9_37" @default.
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