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

Sensors

Yudong Zhang¹ 

[1] School of Information Science and Engineering, Southeast University, Nanjing 210096, China; E-Mail

关键词: fruit classification;    principal component analysis;    color histogram;    Unser's texture analysis;    mathematical morphology;    shape feature;    multi-class SVM;    kernel SVM;    stratified cross validation;   

DOI  :  10.3390/s120912489

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.