## 1 Introduction  
A huge increase of the amount of non-textual information available in electronic form in mass media requires dealing with it as a major source of content [1]. Digital archiving and multimedia analysis are needed to improve the processing efficiency of this material. Specifically, multimedia intelligent systems count on effective classification and indexing of multimedia (e.g., images and videos) in various domains, including sports. The presence of sports in the media has grown exponentially over the last few decades [2], as the broadcasters produce enormous numbers of sports content in cyberspace due to massive viewership and commercial benefits [3]. To deal with such an amount of information, computational intelligence methods are increasingly used in sports [4]. Image classification, as one of the enabling techniques for effective manipulation over large-scale images and videos, is challenging [5]. The intent of the classification process is to decide whether an image belongs to a certain category. Traditional algorithms for sports image classification require carefully hand-crafted features, such as color, texture, object shape, and contour [6]. Notwithstanding the fact that this kind of use is very impractical due to the emergence of massive-scale data, such algorithms have the most difficulty in distinguishing sports disciplines that are very similar in appearance. In recent years, we have witnessed an enormous advancement of deep learning (DL) methods and techniques utilizing convolutional neural networks (CNNs). CNN-based algorithms, which can automatically extract deep representation of training data, have achieved impressive performance in image classification [6], providing results that achieved near-human performance or have even outperformed humans [7]. Various research works have proved that the application of such methods is very encouraging also for sports images classification [8; 9]. However, in order for CNNs to be able to train a good predictive model with high predictive performance, a large enough dataset with reliable ground truth is needed. While the human annotation is impossible for large image datasets, for smaller datasets the manual process also may not be accurate due to human mistakes and perception subjectivity [1]. In the last years, the lack of adequate datasets is most commonly addressed with the utilization of transfer learning approaches [10]. Transfer learning enables one to transfer the weights of a CNN model, previously trained for a specific task, to a new model solving different, but related tasks. With the use of transfer learning, CNN-based classification models of high predictive performance can be produced, even when a large annotated dataset is not available. Furthermore, the training of such models generally requires less time, since the models are already somewhat pre-trained. To fit a CNN model into different problems, its hyper-parameters must be properly tuned [11]. One of the major issues of using CNN for image classification is thus finding the right learning parameters and network topology to achieve the best performance [12]. Namely, there is no general rule to follow which would guarantee a good outcome, so it commonly depends on previous experience and trying out different parameter settings. Selecting the best hyper-parameter configuration often requires deep knowledge of machine learning (ML) algorithms and a lot of burdensome work. To automatize this process, hyper-parameter optimization (HPO) techniques may be the right approach [11]. Based on our previous experience with image classification using CNNs with HPO [13; 14], we set our goals to develop an advanced CNN based image classification method tailored to the task of identifying a sports discipline from a small image dataset of very similar sports, namely American football, rugby, soccer, and field hockey.