Bag-of-Feature model for sweet and bitter almond classification. (April 2017)

• Record Type: Journal Article
• Authors: Nasirahmadi, Abozar; Miraei Ashtiani, Seyed-Hassan
• Is Part Of: Biosystems engineering. Volume 156(2017)
• Page Start: 51
• Page End: 60
• Publication Date: 2017-04
• Subjects: Almond -- Bag-of-Feature -- Classification -- Keypoint -- Machine vision; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2017.01.008
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : The use of computer vision techniques in post-harvest processing of agricultural products has increased considerably in recent years due to their non-destructive and rapid monitoring abilities. Image processing, combined with pattern recognition, has been applied in fruit sorting and classification. In this study, a Bag-of-Feature (BoF) model is used for the classification of 20 sweet and bitter almond varieties. Harris, Harris–Laplace, Hessian, Hessian–Laplace and Maximally Stable Extremal Regions (MSER) keypoint detectors along with a Scale Invariant Feature Transform (SIFT) descriptor are used in the BoF model. The k-means clustering method is applied for building a codebook from keypoint descriptors. The performance of 3 classifiers, which were k-Nearest Neighbour (k-NN), linear and chi-square Support Vector Machine (L-SVM and Chi-SVM, respectively) were compared using classification results in the model. It was observed that the Chi-SVM classifier outperformed the k-NN and L-SVM classifiers. Using the BoF model, it was possible to detect and classify sweet and bitter varieties with high overall accuracy. Highlights: BoF model is proposed for classification of sweet and bitter almonds. Five keypoint detectors and a SIFT descriptor were applied in the model. Size of visual words was examined by accuracy values. Chi-SVM performed better than L-SVM and k-NN classifiers.