## 2 Related Work  
The use of image classification methods and techniques in sports is not new. Previous works on image and video classification for sports related content can be generally divided into traditional ML algorithms and the more recently developed DL techniques [9]. While in the traditional ML approaches one of the major challenges is to define and extract the appropriate features from images, in the case of DL approaches, both the features and the classifier are automatically compiled in the learning process. In [15], the authors proposed a method for classifying sports images, which is based on the Bayesian framework and employs four important color features to exploit the properties of sports images. For this purpose, color histogram, color coherence vector, edge direction histogram, and edge direction coherence vector are extracted from images, on which a Bayesian classifier is trained to automatically assign a class to a sports image. Another well known traditional classification method was used by [PERSON] et al. [16], who proposed a classification method based on the Support Vector Machine (SVM) and key frames features extraction to classify sport video contents from the 1960 Olympic games. For the extracted key frame images, feature vectors were computed in the Laguerre-Gauss transformed domain, which represented the input of the SVM classifier. The SVM was also used for the classification of sports images by [PERSON] et al. [17], who compared it with another traditional classification technique, decision trees, showing the advantage of SVM. To achieve better performance, in general, many low-level features are extracted from images to discriminate different types of images. Beside requiring additional effort and posing a computational overhead, the classification model may also tend to overfit, resulting in poor performance. To avoid such problems in the domain of sports images, in [1], the authors proposed feature dimensionality reduction techniques for the annotation of image datasets. They investigated different techniques, such as Chi-square, information gain, gain ratio, and Latent Semantic Analysis (LSA), and applied them for sports images classification using a SVM classifier. A comparison between the performances of applying SVM alone and when incorporating the different reduction methods showed a raise in the accuracy. An early attempt to move from traditional ML approaches to neural networks was made by [PERSON] et al. [18], who trained a shallow neural network following a domain-dependent ontology to perform classification, resulting in a scalable system capable of examining images and classifying them based on their visual content. After classifying the objects within an image into predefined classes, the ontology is traversed in order to find a possible semantic relationship among those objects. An extensive evaluation of CNNs on video classification is presented in [8], where the authors used a dataset of one million YouTube sports videos to train a CNN. Their best produced spatiotemporal networks display significant performance improvements compared to strong feature-based baselines. The research included a transfer learning approach, where the authors studied the generalization performance of their best model by retraining the top layers on the UCF-101 Action Recognition dataset and observed significant performance improvements compared to the UCF-101 baseline model. A recent interesting approach to shot classification of field sports videos is presented in [3], where the authors proposed an effective shot classification method based on AlexNet CNN. The method proposes a neural network consisting of five convolutional and three fully connected layers to classify the shots into long, medium, close-up, and out-of-the-field shots. Through the response normalization and the dropout layers on the feature maps, the overall training and validation performance evaluated over a diverse dataset of cricket and soccer videos was boosted.