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

By Yudong Zhang, Lenan Wu. Italian National Conference on Sensors, 2012

324 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/33fb28db520ac597bd694c96e1766eaea056153e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Shuihua Wang
2. Preetha Phillips
3. Genlin Ji
4. Yudong Zhang

In Atmospheric Science

1. Shuihua Wang
2. Preetha Phillips
3. Genlin Ji
4. Yudong Zhang

In Media Technology

1. Shuihua Wang
2. Preetha Phillips
3. Genlin Ji
4. Yudong Zhang

Citations in the corpus

• Binary PSO with mutation operator for feature selection using decision tree applied to spam detection (2014, methodology): “The parameters of SVM were trained by quadratic programming method [41].”
• A Review of Convolutional Neural Network Applied to Fruit Image Processing (2020, unknown): “94%, which is higher than the other methods of machine learning that they applied, that yielded accuracies for the PCA + kSVM [65] of 88.”
• Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation (2019, unknown): “For example, the PCA + kSVM [37] only obtains an accuracy of 88.”