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Action Recognition Using Context-Constrained Linear Coding
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AI
DOI：10.1109/LSP.2012.2191615.png)
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Abstract
En 中文
Although traditional bag-of-words model has shown promising results for action recognition, it takes no consideration of the relationship among spatio-temporal points; furthermore, it also suffers serious quantization error. In this letter, we propose a novel coding strategy called context-constrained linear coding (CLC) to overcome these limitations. We first calculate the contextual distance between local descriptors and each codeword by considering the spatio-temporal contextual information. Then, linear coding using contextual distance is adopted to alleviate the quantization error. Our method is verified on two challenging databases (KTH and UCF sports), and the experimental results demonstrate that our method achieves better results than previous methods in action recognition.
Keywords:
Action recognition
context-constrained linear coding
contextual distance
Journal
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