The User-Pleasant Video Skimming by Multi-Modal Keywords Semantics

Yiqing Shen

doi:10.20944/preprints201812.0086.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 5 December 2018 / Approved: 6 December 2018 / Online: 6 December 2018 (13:19:57 CET)
Version 4 : Received: 1 August 2019 / Approved: 5 August 2019 / Online: 5 August 2019 (03:48:49 CEST)

In this paper, we propose a novel approach of video skimming by exploiting the fusion of video temporal information and keyword information representation extracted from multi-model video information including audio, text and visual indices. In addition, we introduce the brand-safe filtering and sentiment analysis in order to only reserve the user-friendly content in the video skim. In the experiment by using the videos from YouTube-8M dataset, we have proved that the semantic conservation in the video skim from the proposed approach highly outperforms the approaches by only partial information of the video in conserving the semantic content of the video.

Multi-model information fusion, Video skimming, Audio and text classification, keyframe extraction

Computer Science and Mathematics, Computer Science

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The User-Pleasant Video Skimming by Multi-Modal Keywords Semantics

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