Abstract
This paper investigates the accuracy of five state-of-the-art machine learning techniques — decision tree J48, nearest neighbors, k-nearest neighbors (k-NN), backpropagation neural network, and naive Bayes — for image-based snake species identification problem. Conventionally, snake species identification is conducted manually based on the observation of the characteristics such head shape, body pattern, body color, and eyes shape. Images of 22 species of snakes that can be found in Malaysia were collected into a database, namely the Snakes of Perlis Corpus. Then, an intelligent approach is proposed to automatically identify a snake species based on an image which is useful for content retrieval purpose where a snake species can be predicted whenever a snake image is given as input. Our experiment shows that backpropagation neural network and nearest neighbour are highly accurate with greater than 87 % accuracy on CEDD descriptor in this problem.
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Authors would like to thank the Taman Ular & Reptilia, Perlis and the School of Computer and Communication Engineering for the facilities provided in conducting this research.
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Amir, A., Zahri, N.A.H., Yaakob, N., Ahmad, R.B. (2017). Image Classification for Snake Species Using Machine Learning Techniques. In: Phon-Amnuaisuk, S., Au, TW., Omar, S. (eds) Computational Intelligence in Information Systems. CIIS 2016. Advances in Intelligent Systems and Computing, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-319-48517-1_5
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DOI: https://doi.org/10.1007/978-3-319-48517-1_5
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