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2007
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Learning Motion Categories using both Semantic and Structural Information

11 years 20 days ago
Learning Motion Categories using both Semantic and Structural Information
Current approaches to motion category recognition typically focus on either full spatiotemporal volume analysis (holistic approach) or analysis of the content of spatiotemporal interest points (part-based approach). Holistic approaches tend to be more sensitive to noise e.g. geometric variations, while part-based approaches usually ignore structural dependencies between parts. This paper presents a novel generative model, which extends probabilistic latent semantic analysis (pLSA), to capture both semantic (content of parts) and structural (connection between parts) information for motion category recognition. The structural information learnt can also be used to infer the location of motion for the purpose of motion detection. We test our algorithm on challenging datasets involving human actions, facial expressions and hand gestures and show its performance is better than existing unsupervised methods in both tasks of motion localisation and recognition.
Shu-Fai Wong, Tae-Kyun Kim, Roberto Cipolla
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Shu-Fai Wong, Tae-Kyun Kim, Roberto Cipolla
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