Abstract
The potential to positively influence research developments in seemingly unrelated areas leads to an increasing interest in the analysis of video games. As game publishers rarely provide an open interface to gain access to in-game information, the proposed system relies on the availability of video game recordings and broadcasts and operates completely in the visual domain. The classification of video game icons and associated metadata serves as an example task to assess the potential of several image recognition methods, including Random Forests (RFs), Support Vector Machines (SVMs), and Convolutional Networks (ConvNets). The experiments show that all machine learning approaches are able to successfully classify game icons in their original state, but performance is significantly decreased for icons in a cooldown state. SVMs fail to estimate the correct cooldown state, while RFs are outperformed by ConvNets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wagner, M.G.: On the scientific relevance of eSports. In: International Conference on Internet Computing, pp. 437–442 (2006)
Esports Market Report: Courtside-Playmakers of 2017. https://www.superdataresearch.com/market-data/esports-market-report/
Blattner, M., Sumikawa, D., Greenberg, R.: Earcons and icons: their structure and common design principles. Hum. Comput. Interact. 4, 11–44 (1989)
Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical Report, Department of Computer Science, University of Toronto, (2009)
Song, Y.: Real-Time Video Highlights for Yahoo Esports (2016). arXiv preprint: arXiv:1611.08780
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Hughes, M., Bartlett, R.: The use of performance indicators in performance analysis. J. Sports Sci. 20, 739–754 (2002)
Spann, M., Skiera, B.: Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters. J. Forecast. 28, 55–72 (2009)
Yang, Y., Qin, T., Lei, Y.H.: Real-time eSports Match Result Prediction (2016). arXiv preprint: arXiv:1701.03162
Cireşan, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)
Semenov, A., Romov, P., Korolev, S., Yashkov, D., Neklyudov, K.: Performance of machine learning algorithms in predicting game outcome from drafts in Dota 2. In: International Conference on Analysis of Images, Social Networks and Texts, pp. 26–37 (2016)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Ebrahimzadeh, R., Jampour, M.: Efficient handwritten digit recognition based on histogram of oriented gradients and SVM. Int. J. Comput. Appl. 104, 10–13 (2014)
Kleinberg, E.M.: An overtraining-resistant stochastic modeling method for pattern recognition. Ann. Stat. 24(6), 2319–2349 (1996)
Zhang, B., Srihari, S.: Fast k-nearest neighbor classification using cluster-based trees. IEEE Trans. Pattern Anal. Mach. Intell. 26, 525–528 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Eichelbaum, J., Hänsch, R., Hellwich, O. (2018). Classification of Icon Type and Cooldown State in Video Game Replays. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-93000-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92999-6
Online ISBN: 978-3-319-93000-8
eBook Packages: Computer ScienceComputer Science (R0)