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Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements and 2) the localization and classification of actions have to be performed even before they are fully observed. We propose a new random forest (RF)-based online action detection framework that addresses these challenges. Our...
We present an efficient semantic segmentation algorithm based on contextual information which is constructed using super pixel-level cues. Although several semantic segmentation algorithms employing super pixel-level cues have been proposed and significant technical advances have been achieved recently, these algorithms still suffer from inaccurate super pixel estimation, recognition failure, time...
This paper studies a method for learning a discriminative visual codebook for various computer vision tasks such as image categorization and object recognition. The performance of various computer vision tasks depends on the construction of the codebook which is a table of visual-words (i.e. codewords). This paper proposed a learning criterion for constructing a discriminative codebook, and it is...
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