Mapping sports interest with social network

  • Gabriel Souza Ferreira UIT
  • Flávio Luis Cardeal Pádua CEFET-MG
  • William Robson Schwartz UFMG
  • Marco Túlio Alves Nolasco Rodrigues UIT


Discovering regions that have sports interest in a set of images acquired from a scene at different times and possibly from different viewpoints and cameras is a crucial step for many applications. Physical activity can be effective at all stages of chronic disease, therefore, finding regions with the presence of physical activities might contribute to is important for the elaboration of public policies to minimize the presence of diseases such as obesity. This work addresses the problem of sport/non-sport image classification. We combine Convolutional Neural Network (CNN), traditional classifiers and geographical information to provide robust training and testing stages. As result, we achieved a high area under the curve (AUC) in a social network dataset. The experimental results show the feasibility of our proposed model. These results can be used and applied to develop public health policies based on statistics of sports interest.


(2018). Centers for disease control and prevention (cdc).

Barbier, G. and Liu, H. (2011). Data mining in social media. In Social network data analytics, pages 327–352. Springer.

Blair, S. N. (2009). Physical inactivity: the biggest public health problem of the 21st century. British journal of sports medicine, 43(1):1–2.

Blair, S. N., Brodney, S., et al. (1999). Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues. Medicine and science in sports and exercise, 31:S646–S662.

Boland, M. V. and Murphy, R. F. (2001). A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of hela cells. Bioinformatics, 17(12):1213–1223.

Bouchard, C. E., Shephard, R. J., and Stephens, T. E. (1994). Physical activity, fitness, and health: International proceedings and consensus statement. In International Consensus Symposium on Physical Activity, Fitness, and Health, 2nd, May, 1992, Toronto, ON, Canada. Human Kinetics Publishers.

Charfi, I., Miteran, J., Dubois, J., Atri, M., and Tourki, R. (2012). Definition and performance evaluation of a robust svm based fall detection solution. In Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on, pages 218–224. IEEE.

Cherkassky, V. and Mulier, F. M. (2007). Learning from data: concepts, theory, and methods. John Wiley & Sons.

Chodzko-Zajko, W. J., Proctor, D. N., Singh, M. A. F., Minson, C. T., Nigg, C. R., Salem, G. J., and Skinner, J. S. (2009). Exercise and physical activity for older adults. Medicine & science in sports & exercise, 41(7):1510–1530.

Ciocca, G., Cusano, C., and Schettini, R. (2015). Image orientation detection using lbp-based features and logistic regression. Multimedia Tools and Applications, 74(9):3013–3034.

Committee, P. A. G. A. et al. (2008). Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services, 2008:A1–H14.

Ferwerda, B., Schedl, M., and Tkalcic, M. (2016). Using instagram picture features to predict users’ personality. In International Conference on Multimedia Modeling, pages 850–861. Springer.

GUGULOTHU, V. K. and RAO, S. M. (2016). Classification of sports images using naive bayesian classifier. International Journal of Engineering Technology and Computer Research, 4(4).

Guillermo Rivera, M. L. d. A. and RSSSF (2017). Sao paulo state stadia - rsssf brazil.

Hagan, M. T., Demuth, H. B., Beale, M. H., et al. (1996). Neural network design, volume 20. Pws Pub. Boston.

Harrell, F. E. (2001). Ordinal logistic regression. In Regression modeling strategies, pages 331–343. Springer.

Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., and Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18–28.

Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al. (2003). A practical guide to support vector classification.

Jose, A. S. and Hernandez, E. (2017). City-scale mapping of pets using georeferenced images. SIGSPATIAL Special, 8(3):5–6.

Kagaya, H. and Aizawa, K. (2015). Highly accurate food/non-food image classification based on a deep convolutional neural network. In International Conference on Image Analysis and Processing, pages 350–357. Springer.

Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.

LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.

Lu, D. and Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 28(5):823–870.

Maas, A. L., Hannun, A. Y., and Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models. In Proc. icml, volume 30, page 3.

McAuley, E. (1994). Physical activity, fitness, and health: The consensus knowledge. Champaign, IL: Human Kinetics. organization (WHO, W. H. et al. (2017). Global health risks-mortality and burden of disease attributable to selected major risks. Cancer.

Pratt, M., Norris, J., Lobelo, F., Roux, L., and Wang, G. (2014). The cost of physical inactivity: moving into the 21st century. Br J Sports Med, 48(3):171–173.

Refaeilzadeh, P., Tang, L., and Liu, H. (2009). Cross-validation. In Encyclopedia of database systems, pages 532–538. Springer.

Rejani, Y. and Selvi, S. T. (2009). Early detection of breast cancer using svm classifier technique. arXiv preprint arXiv:0912.2314.

Sallis, J. F., Frank, L. D., Saelens, B. E., and Kraft, M. K. (2004). Active transportation and physical activity: opportunities for collaboration on transportation and public health research. Transportation Research Part A: Policy and Practice, 38(4):249–268.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818–2826.

Wasserman, S. and Faust, K. (1994). Social network analysis: Methods and applications, volume 8. Cambridge university press.

Yadav, J. S., Yadav, M., and Jain, A. (2014). Artificial neural network. International Journal of Scientific Research and Education, 1(6):108–117.

Zheng, A. (2015). Evaluating Machine Learning Models A Beginner’s Guide to Key Concepts and Pitfalls.

Como Citar

Selecione um Formato
FERREIRA, Gabriel Souza; PÁDUA, Flávio Luis Cardeal; SCHWARTZ, William Robson; RODRIGUES, Marco Túlio Alves Nolasco. Mapping sports interest with social network. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 506-517. ISSN 2763-9061. DOI: