Abstract
Scene classification carry out an imperative accountability in the current emerging field of automation. Traditional classification methods endure with tedious processing techniques. With the advent of CNN and deep learning models have greatly accelerated the job of scene classification. In our paper we have considered an area of application where the deep learning can be used to assist in the civil and military applications and aid in navigation. Current image classifications concentrate on the various available labeled datasets of various images. This work concentrates on classification of few scenes that contain pictures of people and places that are affected in the areas of flood. This aims at assisting the rescue officials at the need of natural calamities, disasters, military attacks etc. Proposed work explains a classifying system which can categorize the small scene dataset using transfer learning approach. We collected the pictures of scenes from sites and created a small dataset with different flood affected activities. We have utilized transfer learning model, RESNET in our proposed work which showed an accuracy of 88.88% for ResNet50 and 91.04% for ResNet101 and endow with a faster and economical revelation for the application involved.
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References
Kamavisdar, P., Saluja, S., Agrawal, S.: A survey on image classification approaches and techniques. Int. J. Adv. Res. Comput. Commun. Eng. 2(1), 1005–1009 (2013)
Sharma, M., Anuradha, J., Manne, H.K., Kashyap, G.S.C.: Facial detection using deep learning. IOP Conf. Ser. Mater. Sci. Eng. 263, 042092 (2017)
Bandhu, A., Sekhar Roy, S.: Classifying multi-category images using deep learning: a convolutional neural network model. In: 2nd IEEE International Conference on Recent Trends in Electronics Information & Communication Technology, pp. 1–6 (2017)
Regina Lourdhu Suganthi, S., Hanumanthappa, M., Kavitha, S.: Event image classification using deep learning. In: 2018 International Conference on Soft-computing and Network Security, pp. 11–17 (2018)
LeCun, Y., Yoshua, B., Hinton, G..: Deep learning. Nature 521(7553), 436–444 (2015)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Weng, Q., et al.: Land-use classification via extreme learning classifier based on deep convolutional features. IEEE Geosci. Remote Sens. Lett. (2017)
Chauhan, K., Ram, S.: Image classification with deep learning and comparison between different convolutional neural network structures using Tensorflow and Keras. Int. J. Adv. Eng. Res. 533–538 (2018)
Zhang, F., Du, B., Zhang, L.: Scene classification via a gradient boosting random convolutional network framework. IEEE Trans. Geosci. Remote Sens. 54(3), 1793–1802 (2016)
Panigrahi, S., Nanda, A., Swarnkar, T.: Deep learning approach for image classification. In: 2018 2nd International Conference on Data Science and Business Analytics (2018)
Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016)
Pasolli, E., Melgani, F., Tuia, D., Pacifici, F., Emery, W.J.: SVM active learning approach for image classification using spatial information. IEEE Trans. Geosci. Remote Sens. 52(4), 2217–2223 (2014)
Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)
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Surendran, R., Anitha, J., Angelopoulou, A., Kapetanios, E., Chausalet, T., Jude Hemanth, D. (2022). Transfer Learning Based Natural Scene Classification for Scene Understanding by Intelligent Machines. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_6
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