Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization
With the exponential growth of the presence of sport in the media, the need for effective classification of sports images has become crucial. The traditional approaches require carefully hand-crafted features, which make them impractical for massive-scale data and less accurate in distinguishing ima...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8494 |
| _version_ | 1797546473164898304 |
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| author | Vili Podgorelec Špela Pečnik Grega Vrbančič |
| author_facet | Vili Podgorelec Špela Pečnik Grega Vrbančič |
| author_sort | Vili Podgorelec |
| collection | DOAJ |
| description | With the exponential growth of the presence of sport in the media, the need for effective classification of sports images has become crucial. The traditional approaches require carefully hand-crafted features, which make them impractical for massive-scale data and less accurate in distinguishing images that are very similar in appearance. As the deep learning methods can automatically extract deep representation of training data and have achieved impressive performance in image classification, our goal was to apply them to automatic classification of very similar sports disciplines. For this purpose, we developed a CNN-TL-DE method for image classification using the fine-tuning of transfer learning for training a convolutional neural network model with the use of hyper-parameter optimization based on differential evolution. Through the automatic optimization of neural network topology and essential training parameters, we significantly improved the classification performance evaluated on a dataset composed from images of four similar sports—American football, rugby, soccer, and field hockey. The analysis of interpretable representation of the trained model additionally revealed interesting insights into how our model perceives images which contributed to a greater confidence in the model prediction. The performed experiments showed our proposed method to be a very competitive image classification method for distinguishing very similar sports and sport situations. |
| first_indexed | 2024-03-10T14:29:58Z |
| format | Article |
| id | doaj.art-8dee104df5494e79a69defcd6d1391d0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:29:58Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8dee104df5494e79a69defcd6d1391d02023-11-20T22:41:03ZengMDPI AGApplied Sciences2076-34172020-11-011023849410.3390/app10238494Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter OptimizationVili Podgorelec0Špela Pečnik1Grega Vrbančič2Institute of Informatics, Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, SloveniaInstitute of Informatics, Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, SloveniaInstitute of Informatics, Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, SloveniaWith the exponential growth of the presence of sport in the media, the need for effective classification of sports images has become crucial. The traditional approaches require carefully hand-crafted features, which make them impractical for massive-scale data and less accurate in distinguishing images that are very similar in appearance. As the deep learning methods can automatically extract deep representation of training data and have achieved impressive performance in image classification, our goal was to apply them to automatic classification of very similar sports disciplines. For this purpose, we developed a CNN-TL-DE method for image classification using the fine-tuning of transfer learning for training a convolutional neural network model with the use of hyper-parameter optimization based on differential evolution. Through the automatic optimization of neural network topology and essential training parameters, we significantly improved the classification performance evaluated on a dataset composed from images of four similar sports—American football, rugby, soccer, and field hockey. The analysis of interpretable representation of the trained model additionally revealed interesting insights into how our model perceives images which contributed to a greater confidence in the model prediction. The performed experiments showed our proposed method to be a very competitive image classification method for distinguishing very similar sports and sport situations.https://www.mdpi.com/2076-3417/10/23/8494classificationCNNtransfer learningoptimizationmachine learning |
| spellingShingle | Vili Podgorelec Špela Pečnik Grega Vrbančič Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization Applied Sciences classification CNN transfer learning optimization machine learning |
| title | Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization |
| title_full | Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization |
| title_fullStr | Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization |
| title_full_unstemmed | Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization |
| title_short | Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization |
| title_sort | classification of similar sports images using convolutional neural network with hyper parameter optimization |
| topic | classification CNN transfer learning optimization machine learning |
| url | https://www.mdpi.com/2076-3417/10/23/8494 |
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