Image modeling towards sport scenes plays an important role in sport image classification and analysis. Traditional algorithms for sport image modeling required carefully hand-crafted features, which cannot be popularized in practical application, especially with the emergence of massive-scale data. Weakly-supervised learning algorithms have shown effectiveness in modeling data with image-level labels. Thus, in this paper, we propose a weakly-supervised learning based method for sport image modeling without utilizing bounding box annotations, which can be used for various sport image applications. More specifically, we first collect large-scale sport images from existing datasets and Internet, and we annotate them at image-level labels. Subsequently, we leverage region proposal generation algorithm to select discriminative regions that can effectively represent the category of images. Each region is fed into a pre-trained CNN architecture to extract deep representation. Afterwards, we design an improved multiple discriminant analysis (MDA) algorithm to project these datapoints to a subspace that can more easily to distinguish different sport categories. Comprehensive experiments have shown the effectiveness and robustness of our proposed method.

针对运动场景的图像建模在运动图像分类和分析中起着重要作用。传统的运动图像建模算法需要精心设计的手工功能，这些功能在实际应用中无法普及，尤其是随着大规模数据的出现。弱监督的学习算法已显示出在使用图像级标签建模数据方面的有效性。因此，在本文中，我们提出了一种基于弱监督学习的运动图像建模方法，该方法无需利用边界框注解，该方法可用于各种运动图像应用。更具体地说，我们首先从现有的数据集和Internet收集大型运动图像，然后在图像级标签上对其进行注释。后来，我们利用区域提议生成算法来选择可有效表示图像类别的可区分区域。每个区域都被馈入经过预训练的CNN架构中，以提取深度表示。之后，我们设计了一种改进的多重判别分析（MDA）算法，将这些数据点投射到一个子空间中，该子空间可以更轻松地区分不同的运动类别。全面的实验表明了我们提出的方法的有效性和鲁棒性。