Representing Pairwise Spatial and Temporal Relations for Action Recognition
Representing Pairwise Spatial and Temporal Relations for Action Recognition
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DOI：
10.1007/978-3-642-15549-9_37
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发表时间：
2010-09
期刊：
影响因子：
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通讯作者：
P. Matikainen;M. Hebert;R. Sukthankar
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文献类型：
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作者：
P. Matikainen;M. Hebert;R. Sukthankar
流行的词袋范式的动作识别任务是基于量化的功能，通常在丢弃它们之间的关系的所有信息的成本建立直方图。然而，尽管包括这些关系的有益性质似乎是显而易见的，但在实践中很难找到视频中特征关系的良好表示。我们提出了一种简单且计算效率高的方法来表达量化特征之间的成对关系，该方法将区分表示的能力与朴素贝叶斯的关键方面相结合。我们展示了我们的技术如何增强基于外观和运动的功能，并且它显着提高了这两种类型的功能的性能。
The popular bag-of-words paradigm for action recognition tasks is based on building histograms of quantized features, typically at the cost of discarding all information about relationships between them. However, although the beneficial nature of including these relationships seems obvious, in practice finding good representations for feature relationships in video is difficult. We propose a simple and computationally efficient method for expressing pairwise relationships between quantized features that combines the power of discriminative representations with key aspects of Naïve Bayes. We demonstrate how our technique can augment both appearance- and motion-based features, and that it significantly improves performance on both types of features.