Discriminative Action Recognition Using Supervised Latent Topic Model
2012 ◽
Vol 190-191
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pp. 1125-1128
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We present a discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure which is most relevant to action categorization. We test our algorithm on two challenging datasets. Experimental results demonstrate the effectiveness of our algorithm.
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2019 ◽
Vol 16
(1)
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pp. 172988141882509
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2020 ◽
Vol 34
(11)
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pp. 2040001
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