Supervised Local Descriptor Learning for Human Action Recognition
Supervised Local Descriptor Learning for Human Action Recognition
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DOI：
10.1109/tmm.2017.2700204
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发表时间：
2017-05
影响因子：
7.3
通讯作者：
Xiantong Zhen;Feng Zheng;Ling Shao;Xianbin Cao;Dan Xu
中科院分区：
文献类型：
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作者：
Xiantong Zhen;Feng Zheng;Ling Shao;Xianbin Cao;Dan Xu
局部特征已被广泛用于计算机视觉任务中，例如，人类行为识别，但它往往是一个极具挑战性的任务，以处理大规模的高维局部特征与冗余信息。在本文中，我们提出了一种新的完全监督的局部描述符学习算法，称为基于图像到类距离（I2CDDE）的判别嵌入方法，以学习紧凑但具有高度判别力的局部特征描述符，以实现更准确和高效的动作识别。通过利用I2C距离的优势，所提出的I2CDDE结合了类标签，以实现对局部特征描述符的完全监督学习，从而实现高度区分但紧凑的局部描述符。我们的I2CDDE的目标是最小化I2C的距离从样本到其相应的类，同时最大化I2C的距离，在低维空间中的其他类。为了进一步提高性能，我们提出将基于图拉普拉斯算子的流形正则化纳入目标函数，通过提取局部内在几何结构来增强嵌入的平滑性。所提出的I2CDDE首次实现了局部特征描述符的完全监督学习。它通过提高局部特征的判别能力，同时通过降维处理大规模数据来大大降低计算负担，从而显着提高了基于I2C的方法的性能。我们将建议的I2CDDE算法应用于四个广泛使用的基准数据集上的人体动作识别。结果表明，I2CDDE可以显着改善基于I2C的分类器，并实现最先进的性能。
Local features have been widely used in computer vision tasks, e.g., human action recognition, but it tends to be an extremely challenging task to deal with large-scale local features of high dimensionality with redundant information. In this paper, we propose a novel fully supervised local descriptor learning algorithm called discriminative embedding method based on the image-to-class distance (I2CDDE) to learn compact but highly discriminative local feature descriptors for more accurate and efficient action recognition. By leveraging the advantages of the I2C distance, the proposed I2CDDE incorporates class labels to enable fully supervised learning of local feature descriptors, which achieves highly discriminative but compact local descriptors. The objective of our I2CDDE is to minimize the I2C distances from samples to their corresponding classes while maximizing the I2C distances to the other classes in the low-dimensional space. To further improve the performance, we propose incorporating a manifold regularization based on the graph Laplacian into the objective function, which can enhance the smoothness of the embedding by extracting the local intrinsic geometrical structure. The proposed I2CDDE for the first time achieves fully supervised learning of local feature descriptors. It significantly improves the performance of I2C-based methods by increasing the discriminative ability of local features while greatly reducing the computational burden by dimensionality reduction to handle large-scale data. We apply the proposed I2CDDE algorithm to human action recognition on four widely used benchmark datasets. The results have shown that I2CDDE can significantly improve I2C-based classifiers and achieves state-of-the-art performance.