A semantic-based video scene segmentation using a deep neural network

Hyesung Ji, Danial Hooshyar, Kuekyeng Kim, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Video scene segmentation is very important research in the field of computer vision, because it helps in efficient storage, indexing and retrieval of videos. Achieving this kind of scene segmentation cannot be done by just calculating the similarity of low-level features presented in the video; high-level features should also be considered to achieve a better performance. Even though much research has been conducted on video scene segmentation, most of these studies failed to semantically segment a video into scenes. Thus, in this study, we propose a Deep-learning Semantic-based Scene-segmentation model (called DeepSSS) that considers image captioning to segment a video into scenes semantically. First, the DeepSSS performs shot boundary detection by comparing colour histograms and then employs maximum-entropy-applied keyframe extraction. Second, for semantic analysis, using image captioning that benefits from deep learning generates a semantic text description of the keyframes. Finally, by comparing and analysing the generated texts, it assembles the keyframes into a scene grouped under a semantic narrative. That said, DeepSSS considers both low- and high-level features of videos to achieve a more meaningful scene segmentation. By applying DeepSSS to data sets from MS COCO for caption generation and evaluating its semantic scene-segmentation task results with the data sets from TRECVid 2016, we demonstrate quantitatively that DeepSSS outperforms other existing scene-segmentation methods using shot boundary detection and keyframes. What’s more, the experiments were done by comparing scenes segmented by humans and scene segmented by the DeepSSS. The results verified that the DeepSSS’ segmentation resembled that of humans. This is a new kind of result that was enabled by semantic analysis, which was impossible by just using low-level features of videos.

Original languageEnglish
Pages (from-to)833-844
Number of pages12
JournalJournal of Information Science
Issue number6
Publication statusPublished - 2019 Dec 1

Bibliographical note

Publisher Copyright:
© The Author(s) 2018.


  • Deep learning
  • image captioning
  • keyframe extraction
  • shot boundary detection
  • video scene segmentation

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences


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