Statistical Texture Mean-Windowing Feature of Snake Identification

Rosniza Roslan, Nurul Hafeeza Aziz, Nursuriati Jamil, Raseeda Hamzah


Snake identification has been explored in various domains such as the image processing domain. In Malaysia, many of the snake species are non-venomous but still dangerous to the human. Conventionally, snake identification is evaluated by collecting the information from the patient. However, it is very hard and difficult to recognize the venomous and non-venomous snake types. Also, doctors need to inject the anti-venom into the patient which produced the side effect. Therefore, this paper classified the venomous snake of Naja Kaouthia and other venomous snake species. All the image datasets have been captured at Malacca Butterfly & Reptile Sanctuary, Melaka. The statistical vectors are extracted by using the normalized mean-moving windows. The taxonomical statistical texture vectors of snake region features are classified using Tree, K-Nearest Neighbor, Support Vector Machine, and Naïve Bayes classifiers. Results showed that most of the classifiers produced an accuracy rate of 100%.


snake identification; image processing; K-Nearest Neighbor; Support Vector Machine; Naïve Bayes

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Copyright (c) 2021 Rosniza Roslan, Nurul Hafeeza Aziz, Nursuriati Jamil, Raseeda Hamzah

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License