Action Recognition Using Context-Constrained Linear Coding

Action Recognition Using Context-Constrained Linear Coding
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使用上下文约束线性编码的动作识别

DOI:
10.1109/lsp.2012.2191615
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发表时间:
2012-03
影响因子:
3.9
通讯作者:
Shuang Liu
Shuang Liu
中科院分区:
工程技术2区
文献类型:
--
作者:
Zhong Zhang;Chunheng Wang;Baihua Xiao;Wen Zhou;Shuang Liu

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虽然传统的词袋模型在动作识别方面取得了良好的效果,但它没有考虑时空点之间的关系;此外,它还存在严重的量化误差。在这封信中,我们提出了一种新的编码策略,称为上下文约束线性编码(CLC)来克服这些限制。我们首先通过考虑时空上下文信息计算局部描述符与每个码字之间的上下文距离。然后,采用基于上下文距离的线性编码来减小量化误差。我们的方法在两个具有挑战性的数据库(KTH和UCF sports)上进行了验证,实验结果表明,我们的方法在动作识别方面取得了比以往方法更好的效果。
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.
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