Journal Article

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

Zhang, Yudong, Wu, Lenan

2012

Overview

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.

• Publisher: MDPI AG,Molecular Diversity Preservation International (MDPI)
• Subject: Algorithms / Classification / color histogram / Digital cameras / Discriminant analysis / Fruit - classification / Fruits / Gaussian / Graphs / Image Processing, Computer-Assisted - methods / kernel SVM / Kernels / mathematical morphology / Methods / multi-class SVM / principal component analysis / Principal Component Analysis - methods / Principal components analysis / Sensors / shape feature / Software / stratified cross validation / Support Vector Machine / Support vector machines / Texture / Unser’s texture analysis