Kernel analysis on Grassmann manifolds for action recognition

Mehrtash T. Harandi, Conrad Sanderson, Sareh Shirazi, Brian C. Lovell

Research output: Contribution to journalArticleResearchpeer-review

45 Citations (Scopus)

Abstract

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises withinclass and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Original languageEnglish
Pages (from-to)1906-1915
Number of pages10
JournalPattern Recognition Letters
Volume34
Issue number15
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Keywords

  • Action recognition
  • Graph-embedding discriminant analysis
  • Grassmann manifolds
  • Reproducing kernel Hilbert space

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