Abstract
Many image classification models have been introduced to help tackle the foremost issue of recognition accuracy. Image classification is one of the core problems in Computer Vision field with a large variety of practical applications. Examples include: object recognition for robotic manipulation, pedestrian or obstacle detection for autonomous vehicles, among others. A lot of attention has been associated with Machine Learning, specifically neural networks such as the Convolutional Neural Network (CNN) winning image classification competitions. This work proposes the study and investigation of such a CNN architecture model (i.e. Inception-v3) to establish whether it would work best in terms of accuracy and efficiency with new image datasets via Transfer Learning. The retrained model is evaluated, and the results are compared to some state-of-the-art approaches.
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TensorFlow: Image Recognition (2018). https://www.tensorflow.org/tutorials/image_recognition. Accessed 20 Apr 2018
Christian Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovih, R.: Going Deeper with Convolutions (arXiv.org) (2015)
Lin, M., Chen, Q., Yan, S.: Network in Network. ICLR submission (arXiv.org) (2013)
Nguyen, N.: Computational Collective Intelligence. Springer, Cham (2016)
Arun, P., Katiyar, S.: A CNN based Hybrid approach towards automatic age registration. Geodesy Cartography 62(1), 33–49 (2013)
Rohrer, B.: How do Convolutional Neural Networks work? (2016). http://brohrer.github.io/how_convolutional_neural_networks_work.html. Accessed 20 Apr 2018
ImageNet: About (2016). http://image-net.org/about-overview. Accessed 20 Apr 2018
Gao, Y., Mosalam, K.: Deep transfer learning for image-based structural damage recognition. Comput. Aided Civ. Infrastruct. Eng. (2018)
Larsen-Freeman, D.: Transfer of learning transformed. Lang. Learn. 63, 107–129 (2013)
Krizhevsky, A.: The CIFAR-10 dataset (2009). https://www.cs.toronto.edu/~kriz/cifar.html. Accessed 20 Apr 2018
Vision Caltech: Computational Vision (2018). http://www.vision.caltech.edu/html-files/archive.html. Accessed 20 Apr 2018
Wu, J.: CNN for Dummies. Nanjing University (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE CVPR 2016: Computer Vision and Pattern Recognition (2016)
Mathworks.com: Convolutional Neural Network (2018). https://www.mathworks.com/content/mathworks/www/en/discovery/convolutional-neural-network.html. Accessed 20 Apr 2018
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Hussain, M., Bird, J.J., Faria, D.R. (2019). A Study on CNN Transfer Learning for Image Classification. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_16
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DOI: https://doi.org/10.1007/978-3-319-97982-3_16
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