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Article overview
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Dominant Codewords Selection with Topic Model for Action Recognition | Hirokatsu Kataoka
; Masaki Hayashi
; Kenji Iwata
; Yutaka Satoh
; Yoshimitsu Aoki
; Slobodan Ilic
; | Date: |
2 May 2016 | Abstract: | In this paper, we propose a framework for recognizing human activities that
uses only in-topic dominant codewords and a mixture of intertopic vectors.
Latent Dirichlet allocation (LDA) is used to develop approximations of human
motion primitives; these are mid-level representations, and they adaptively
integrate dominant vectors when classifying human activities. In LDA topic
modeling, action videos (documents) are represented by a bag-of-words (input
from a dictionary), and these are based on improved dense trajectories. The
output topics correspond to human motion primitives, such as finger moving or
subtle leg motion. We eliminate the impurities, such as missed tracking or
changing light conditions, in each motion primitive. The assembled vector of
motion primitives is an improved representation of the action. We demonstrate
our method on four different datasets. | Source: | arXiv, 1605.0324 | Services: | Forum | Review | PDF | Favorites |
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