Supervised manifold learning for image and video classification

Yang Liu, Yan Liu, Chun Chung Chan

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

5 Citations (Scopus)

Abstract

This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize the distance preserving, we propose a novel algorithm called maximum distance embedding (MDE), which aims to maximize the distances between some particular pairs of data points, with the intention of flattening the local nonlinearity and keeping the discriminant information simultaneously in the embedded feature space. Moreover, MDE measures the dissimilarity between data points using L1-norm distance, which is more robust to outliers than widely used Frobenius norm distance. To adapt the nature tensor structure of image and video data, we further propose the multilinear MDE (M2DE). Experiments on various datasets demonstrate that both MDE and M2DE achieve impressive embedding results of image and video data for classification tasks.
Original languageEnglish
Title of host publicationMM'10 - Proceedings of the ACM Multimedia 2010 International Conference
Pages859-862
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2010
Event18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10 - Firenze, Italy
Duration: 25 Oct 201029 Oct 2010

Conference

Conference18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10
CountryItaly
CityFirenze
Period25/10/1029/10/10

Keywords

  • image and video classification
  • l1-norm optimization
  • manifold learning
  • maximum distance embedding
  • multilinear maximum distance embedding
  • supervised learning

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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