Paper Info
Title
Classification of butterfly images with multi-scale local binary patterns
Abstract
Butterflies are classified first according to their outer morphological qualities. It is required to analyze their genital characters when classification according to their outer morphological qualities is not possible. The genital characters of butterflies can be obtained using various chemical substances and methods; however, these processes can only be carried out with some certain expenses. Furthermore, the preparation of genital slides is time-consuming since it requires specific processes. In this study, a computer vision system based on local binary patterns was proposed to alternative conventional diagnostic methods for the diagnosis of butterfly species. 140 images of 14 butterfly species belonging to the family of Styridae are used. The butterfly diagnostic process was carried out by using LBPP, R attributes as inputs for the ANN, SVM and LR classification methods. 100% classification was achieved with macro and micro patterns obtained with LBPP, R for different values of parameter R. As a result, it was seen butterfly wings have different types of micro and macro properties, and LBP has a major advantage in identification of butterfly species.
Year
DOI
Venue
2013
10.1109/SIU.2013.6531283
Signal Processing and Communications Applications Conference
Keywords
Field
DocType
computer vision,image classification,neural nets,support vector machines,ANN,LBPP,R,LR classification method,SVM,Styridae family,butterfly image classification,butterfly species diagnostic method,butterfly wing,chemical substance,computer vision system,genital character analysis,macropattern,micropattern,multiscale local binary pattern,outer morphological quality,butterfly identification,computer vision,local binary Pattern,pattern recognition
Computer vision,Pattern recognition,Computer science,Local binary patterns,Support vector machine,Butterfly,Artificial intelligence,Artificial neural network,Contextual image classification,Macro
Conference
ISSN
ISBN
Citations 
2165-0608
978-1-4673-5561-2
0
PageRank 
References 
Authors
0.34
3
3
Name
Order
Citations
PageRank
Yilmaz Kaya114412.98
Lokman Kayci2362.48
Necmettin Sezgin31048.74