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 convolution neural network to classify animals in animal videos. Convolution neural network is a powerful machine learning tool which is trained using large collection of diverse images. In this paper, we combine convolutional neural network and SVM for classification of animals. In the first stage, frames are extracted from the animal videos. The extracted animal frames are trained using Alex Net pre-trained convolution neural network. Further, the extracted features are fed into multi-class SVM classifier for the purpose of classification. To evaluate the performance of our system we have conducted extensive experimentation on our own dataset of 200 videos with 20 classes, each class containing 10 videos. From the results we can easily observed that the proposed method has achieved good classification rate compared to the works in the literature.
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Manohar, N., Sharath Kumar, Y.H., Kumar, G.H., Rani, R. (2019). Deep Learning Approach for Classification of Animal Videos. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_35
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DOI: https://doi.org/10.1007/978-981-13-2514-4_35
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