Object and action classification with latent variables

Hakan Bilen, Vinay P. Namboodiri, Luc J. Van Gool

Research output: Contribution to conferencePaperpeer-review

17 Citations (SciVal)


In this paper we propose a generic framework to incorporate unobserved auxiliary information for classifying objects and actions. This framework allows us to explicitly account for localisation and alignment of representations for generic object and action classes as latent variables. We approach this problem in the discriminative setting as learning a max-margin classifier that infers the class label along with the latent variables. Through this paper we make the following contributions a) We provide a method for incorporating latent variables into object and action classification b) We specifically account for the presence of an explicit class related subregion which can include foreground and/or background. c) We explore a way to learn a better classifier by iterative expansion of the latent parameter space. We demonstrate the performance of our approach by rigorous experimental evaluation on a number of standard object and action recognition datasets.

Original languageEnglish
Publication statusPublished - 1 Jan 2011
Event2011 22nd British Machine Vision Conference, BMVC 2011 - Dundee, UK United Kingdom
Duration: 29 Aug 20112 Sept 2011


Conference2011 22nd British Machine Vision Conference, BMVC 2011
Country/TerritoryUK United Kingdom

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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