Weakly-supervised large-scale image modeling for sport scenes and its applications

Abstract

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.