Semi-supervised manifold ordinal regression for image ranking

Yang Liu, Yan Lu, Shenghua Zhong, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

28 Citations (Scopus)


In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.
Original languageEnglish
Title of host publicationMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
Number of pages4
Publication statusPublished - 29 Dec 2011
Event19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 - Scottsdale, AZ, United States
Duration: 28 Nov 20111 Dec 2011


Conference19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11
Country/TerritoryUnited States
CityScottsdale, AZ


  • Image ranking
  • Manifold learning
  • Manifold ordinal regression
  • Ordinal regression
  • Semi-supervised learning
  • Semi-supervised manifold ordinal regression

ASJC Scopus subject areas

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


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