Next Article in Journal
A SPR Aptasensor for Detection of Avian Influenza Virus H5N1
Next Article in Special Issue
Simultaneous Measurement of Neural Spike Recordings and Multi-Photon Calcium Imaging in Neuroblastoma Cells
Previous Article in Journal
Unobstructive Body Area Networks (BAN) for Efficient Movement Monitoring
Sensors 2012, 12(9), 12489-12505; doi:10.3390/s120912489
Article

Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine

 and *
Received: 16 July 2012; in revised form: 7 September 2012 / Accepted: 7 September 2012 / Published: 13 September 2012
(This article belongs to the Special Issue Medical & Biological Imaging)
View Full-Text   |   Download PDF [712 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of each image was removed by a split-and-merge algorithm; Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space; Third, principal component analysis (PCA) was used to reduce the dimensions of feature space; Finally, three kinds of multi-class SVMs were constructed, i.e., Winner-Takes-All SVM, Max-Wins-Voting SVM, and Directed Acyclic Graph SVM. Meanwhile, three kinds of kernels were chosen, i.e., linear kernel, Homogeneous Polynomial kernel, and Gaussian Radial Basis kernel; finally, the SVMs were trained using 5-fold stratified cross validation with the reduced feature vectors as input. The experimental results demonstrated that the Max-Wins-Voting SVM with Gaussian Radial Basis kernel achieves the best classification accuracy of 88.2%. For computation time, the Directed Acyclic Graph SVMs performs swiftest.
Keywords: fruit classification; principal component analysis; color histogram; Unser’s texture analysis; mathematical morphology; shape feature; multi-class SVM; kernel SVM; stratified cross validation fruit classification; principal component analysis; color histogram; Unser’s texture analysis; mathematical morphology; shape feature; multi-class SVM; kernel SVM; stratified cross validation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Zhang, Y.; Wu, L. Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine. Sensors 2012, 12, 12489-12505.

AMA Style

Zhang Y, Wu L. Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine. Sensors. 2012; 12(9):12489-12505.

Chicago/Turabian Style

Zhang, Yudong; Wu, Lenan. 2012. "Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine." Sensors 12, no. 9: 12489-12505.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert