Action recognition with approximate sparse coding
Action recognition with approximate sparse coding
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DOI：
10.1109/icip.2015.7350903
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发表时间：
2015-12
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影响因子：
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通讯作者：
Yu Wang;Jien Kato
中科院分区：
文献类型：
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作者：
Yu Wang;Jien Kato
在本文中，我们提出了一种新的特征编码方法称为近似稀疏编码（ASC）。ASC在离线学习阶段使用稀疏编码（SC）计算大量原型描述符的稀疏代码;并在编码阶段使用近似最近邻（ANN）搜索查找每个待编码描述符的最近原型的稀疏代码。它具有SC的低维性和ANN的快速性，这两者都是人类动作识别任务所需的属性。我们在流行的HMDB 51数据集上对ASC进行了过度评估，并证实它能够有效地将大量视频特征编码为有区别的低维表示。
In this paper, we present a novel feature encoding approach called Approximate Sparse Coding (ASC). ASC computes the sparse codes for a large collection of prototype descriptors in the off-line learning phase with Sparse Coding (SC); and look up the nearest prototype's sparse code for each to-be-encoded descriptor in the encoding phase with Approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the fast speed of ANN, which are both desired properties for the human action recognition task. We excessively evaluated ASC on the popular HMDB51 dataset, and confirme it is able to encode large number of video features into discriminative low dimensional representations efficiently.