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