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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition
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Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition

机译：基于样条回归的半监督特征选择用于视频语义识别

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摘要

To improve both the efficiency and accuracy of video semantic recognition, we can perform feature selection on the extracted video features to select a subset of features from the high-dimensional feature set for a compact and accurate video data representation. Provided the number of labeled videos is small, supervised feature selection could fail to identify the relevant features that are discriminative to target classes. In many applications, abundant unlabeled videos are easily accessible. This motivates us to develop semisupervised feature selection algorithms to better identify the relevant video features, which are discriminative to target classes by effectively exploiting the information underlying the huge amount of unlabeled video data. In this paper, we propose a framework of video semantic recognition by semisupervised feature selection via spline regression . Two scatter matrices are combined to capture both the discriminative information and the local geometry structure of labeled and unlabeled training videos: A within-class scatter matrix encoding discriminative information of labeled training videos and a spline scatter output from a local spline regression encoding data distribution. An -norm is imposed as a regularization term on the transformation matrix to ensure it is sparse in rows, making it particularly suitable for feature selection. To efficiently solve , we develop an iterative algorithm and prove its convergency. In the experiments, three typical tasks of video semantic recognition, such as video concept detection, video classification, and human action recognition, are used to demonstrate that the proposed achieves better performance compared with the state-of-the-art methods.
机译：为了提高视频语义识别的效率和准确性，我们可以对提取的视频特征执行特征选择，以从高维特征集中选择特征子集，以实现紧凑而准确的视频数据表示。如果标记视频的数量很少，则监督性功能选择可能无法识别与目标类别有区别的相关功能。在许多应用中，可以轻松访问大量未标记的视频。这激励我们开发半监督特征选择算法，以更好地识别相关视频特征，这些特征通过有效利用大量未标记视频数据背后的信息来区分目标类别。在本文中，我们提出了一种通过样条回归进行半监督特征选择的视频语义识别框架。结合两个散点矩阵以捕获判别信息和标记和未标记训练视频的局部几何结构：类内散点矩阵编码标记训练视频的判别信息和局部样条回归编码数据分布的样条散点输出。 -norm作为变换矩阵的正则化项而施加，以确保其稀疏成行，使其特别适合于特征选择。为了有效解决，我们开发了一种迭代算法并证明了其收敛性。在实验中，视频语义识别的三个典型任务，例如视频概念检测，视频分类和人体动作识别，被用来证明与现有技术相比，该方法具有更好的性能。

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