Graph-Based Embedding Improvement Feature Distribution in Videos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Video understanding is a significant computer vision research subject since online video content is growing exponentially. Feature extraction and representation play a crucial role in video understanding tasks such as classification, segmentation, and recognition. However, the model's learning is ambiguous since adjacent video frames typically have similar RGB features. To address this issue, we present graph-based embedding to enhance video feature distribution. We construct a graph-structured of videos by connecting similar features. Node embedding is generated by utilizing a graph model. Experiments demonstrate that our approach effectively improves feature distribution. The graph attention network (GAT) improves accuracy and editing score by 4% over the visual model.

Original languageEnglish
Title of host publication2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages435-436
Number of pages2
ISBN (Electronic)9798350324174
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
Duration: 2023 Jul 172023 Jul 19

Publication series

Name2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Country/TerritoryTaiwan
CityPingtung
Period23-07-1723-07-19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Systems
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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