Multi-view manifold learning for media interestingness prediction

Yang LIU, Zhonglei Gu, Yiu Ming CHEUNG, Kien A. Hua

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Media interestingness prediction plays an important role in many real-world applications and attracts much research attention recently. In this paper, we aim to investigate this problem from the perspective of supervised feature extraction. Specifically, we design a novel algorithm dubbed Multi-view Manifold Learning (M2L) to uncover the latent factors that are capable of distinguishing interesting media data from non-interesting ones. By modelling both geometry preserving criterion and discrimination maximization criterion in a unified framework, M2L learns a common subspace for data from multiple views. The analytical solution of M2 L is obtained by solving a generalized eigen-decomposition problem. Experiments on the Predicting Media Interestingness Dataset validate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationICMR 2017 - Proceedings of the 2017 ACM International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages308-314
Number of pages7
ISBN (Electronic)9781450347013
DOIs
Publication statusPublished - 6 Jun 2017
Event17th ACM International Conference on Multimedia Retrieval, ICMR 2017 - Bucharest, Romania
Duration: 6 Jun 20179 Jun 2017

Publication series

NameICMR 2017 - Proceedings of the 2017 ACM International Conference on Multimedia Retrieval

Conference

Conference17th ACM International Conference on Multimedia Retrieval, ICMR 2017
Country/TerritoryRomania
CityBucharest
Period6/06/179/06/17

Scopus Subject Areas

  • Human-Computer Interaction
  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Media interestingness analysis
  • Multi-view manifold learning

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