Abstract
Advances in GPU, parallel computing, and deep neural network made rapid growth in the field of machine learning and computer vision. In this paper, we try to explore the convolution neural network to classify animals. The convolution neural network is a powerful machine learning tool which is trained using a large collection of diverse images. In this paper, we combine convolutional neural network and SVM for classification of animals. The animal images are trained using AlexNet pretrained convolution neural network. Further, the extracted features are fed into multiclass SVM classifier for the purpose of classification. To evaluate the performance of our system, we have conducted extensive experimentation on our own dataset of 5000 images with 50 classes, each class containing 100 images. From the results, we can easily observe that the proposed method has achieved a good classification rate compared to the works in the literature.
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References
Lahiri M, Tantipathananandh C, Warungu R (2011) Biometric animal databases from field photographs: identification of individual Zebra in the wild. In: Proceedings of the ACM international conference on multimedia retrieval (ICMR 2011)
Ardovini A, Cinque L, Sangineto E (2007) Identifying Elephant photos by multi-curve matching. In: Pattern recognition, pp 1867–1877
Ramanan D, Forsyth DA, Barnard K (2006) Building models of animals from videos. IEEE Trans Pattern Anal Mach Intell 28(8):1319–1334
Zeppelzauer M (2013) Automated detection of Elephants in wildlife video. J Image Video Process
Sharath Kumar YH, Manohar N, Chethan HK, Hemantha Kumar G (2014) Animal classification system: a block based approach. In: International conference on information and communication technologies (ICICT 2014)
Manohar N, Kumar YHS, Kumar GH (2016) Supervised and unsupervised learning in animal classification. In: International conference on advances in computing, communications and informatics (ICACCI), Jaipur, pp 156–161
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICLR
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol 1, pp 1097–1105
Elleuch M, Maalej R, Kherallah M (2016) A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. ELSEVIER Procedia Comput Sci 80:1712–1723
Nagi J, Di Caro GA, Giusti A, Nagi F, Gambardella LM (2012) Convolutional neural support vector machines: hybrid visual pattern classifiers for multi-robot systems. In: 11th international conference on machine learning and applications, pp 27–32
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd ed. Wiley-Interscience
Hsu Chih-Wei, Lin Chih-Jen (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425
Shoba Rani N, Chandan N, Sajan Jain A, Kiran HR (2018) Deformed character recognition using convolutional neural networks. Int J Eng Technol 7(3)
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Manohar, N., Kumar, Y.H.S., Rani, R., Kumar, G.H. (2019). Convolutional Neural Network with SVM for Classification of Animal Images. In: Sridhar, V., Padma, M., Rao, K. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-13-5802-9_48
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DOI: https://doi.org/10.1007/978-981-13-5802-9_48
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