Clustering on Grassmann manifolds via kernel embedding with application to action analysis

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

34 Citations (Scopus)


With the aim of improving the clustering of data (such as image sequences) lying on Grassmann manifolds, we propose to embed the manifolds into Reproducing Kernel Hilbert Spaces. To this end, we define a measure of cluster distortion and embed the manifolds such that the distortion is minimised. We show that the optimal solution is a generalised eigenvalue problem that can be solved very efficiently. Experiments on several clustering tasks (including human action clustering) show that in comparison to the recent intrinsic Grassmann k-means algorithm, the proposed approach obtains notable improvements in clustering accuracy, while also being several orders of magnitude faster.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Number of pages4
Publication statusPublished - 1 Dec 2012
Externally publishedYes
EventIEEE International Conference on Image Processing 2012 - Coronado Springs - Disney World, Orlando, United States of America
Duration: 30 Sept 20123 Oct 2012
Conference number: 19th (Proceedings)

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


ConferenceIEEE International Conference on Image Processing 2012
Abbreviated titleICIP 2012
Country/TerritoryUnited States of America
Internet address


  • action analysis
  • clustering
  • Grassmann manifolds
  • kernels
  • Reproducing Kernel Hilbert Spaces

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