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Time-Sensitive Topic Models for Action Recognition in Videos

Abstract : In this paper, we postulate that temporal information is important for action recognition in videos. Keeping temporal information, videos are represented as word time documents. We propose to use time-sensitive probabilistic topic models and we extend them for the context of supervised learning. Our time-sensitive approach is compared to both PLSA and Bag-of-Words. Our approach is shown to both capture semantics from data and yield classification performance comparable to other methods, outperforming them when the amount of training data is low.
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Submitted on : Tuesday, February 21, 2017 - 10:46:48 PM
Last modification on : Thursday, February 7, 2019 - 5:55:47 PM


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  • HAL Id : hal-00872048, version 1


Romain Tavenard, Rémi Emonet, Jean-Marc Odobez. Time-Sensitive Topic Models for Action Recognition in Videos. ICIP - International Conference on Image Processing, Sep 2013, Melbourne, Australia. ⟨hal-00872048⟩



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