Snake classification from images
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Taxonomy, Zoology
- Keywords
- Taxonomy, Snake features, Snake classification, Snake images, classifiers
- Copyright
- © 2017 James
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Snake classification from images. PeerJ Preprints 5:e2867v1 https://doi.org/10.7287/peerj.preprints.2867v1
Abstract
Incorrect snake identification from the observable visual traits is a major reason of death resulting from snake bites. So far no automatic classification method has been proposed to distinguish snakes by deciphering the taxonomy features of snake for the two major species of snakes i.e. Elapidae and Viperidae. We present a parallel processed inter-feature product similarity fusion based automatic classification of Spectacled Cobra, Russel's Viper, King Cobra, Common Krait, Saw Scaled Viper, Hump nosed Pit Viper. We identify 31 different taxonomically relevant features from snake images for automated snake classification studies. The scalability and real-time implementation of the classifier is analyzed through GPU enabled parallel computing environment. The developed systems finds application in wild life studies, analysis of snake bites and in management of snake population.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
SnakeImageData_Samples
The sample snake image dataset consisting of Viper and Cobra snake classes.