Instructive Video retrieval based on hybrid ranking and attribute learning: A case study on surgical skill training

Lin Chen, Peng Zhang, Baoxin Li

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

4 Scopus citations

Abstract

Video-based systems have been increasingly used in various training tasks in applications like sports, dancing, and surgery. One key task to add automation to such systems is to automatically select reference videos for a given training video of a trainee. In this paper, we formulate a new problem of instructive video retrieval and propose a solution using both attribute learning and learning to rank. The method first evaluates a user's skill attributes by relative attribute learning. Then, the most critical skill attribute in need of improvement is selected and reported to the user. Finally, a hybrid ranking learning to rank method is employed to retrieve instructive videos from a dataset, which serve as reference for the user. Two main technical problems are solved in this method. First, we combine both skill and visual feature to characterize skill superiority and context similarity. Second, we propose a hybrid ranking approach that works with both pair-wise and point-wise labels of the data. The benefit of the proposed method over other heuristic methods is demonstrated by both objective and subjective experiments, using surgical training videos as a case study.

Original languageEnglish (US)
Title of host publicationMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages1045-1048
Number of pages4
ISBN (Electronic)9781450330633
DOIs
StatePublished - Nov 3 2014
Event2014 ACM Conference on Multimedia, MM 2014 - Orlando, United States
Duration: Nov 3 2014Nov 7 2014

Publication series

NameMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia

Other

Other2014 ACM Conference on Multimedia, MM 2014
CountryUnited States
CityOrlando
Period11/3/1411/7/14

Keywords

  • Attribute learning
  • Learning to rank
  • Video retrieval

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Software

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