Image processing for snake indentification based on bite using Local Binary Pattern and Support Vector Machine method

Abstract

False identification snake bite from observation of visual features is a dangerous thing, because if it is get wrong in handling of first aid against poisonous snake bites, it would be result in death. In Indonesia there are 348 types of venomous snakes, of course with a very large number of it must be wary if bitten by snake. In general, ordinary people with snake bite patterns will panic and do not know what to do for first aid if they are bitten by snake. Even a doctor can be wrong in identifying snake bites. Difficulty of identifying snake directly, then made an Image Processing System for Identification snake bite using Local Binary Pattern and Support Vector Machine method that helps identify and classify snakes automatically. This system classifies venomous and non-venomous snake based on its bite pattern image using two methods ie Support Vector Machines(SVM) and Local binary Pattern(LBP). Final result from this research yielded 89% accuracy with P=8, r=1 for LBP and C=1, Ϭ=0.15 for SVM using the RBF kernel. The dataset used has 28 data, the data divided into 19 training data and 9 test data.