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Fruit classification based on weighted score-level feature fusion

[+] Author Affiliations
Hulin Kuang, Leanne Lai Hang Chan, Hong Yan

City University of Hong Kong, Department of Electronic Engineering, 83 Tat Chee Avenue, Kowloon, Hong Kong

Cairong Liu

Chinese University of Hong Kong, Department of Mathematics, Tai Po Road, Shatin, NT, Hong Kong

J. Electron. Imaging. 25(1), 013009 (Jan 19, 2016). doi:10.1117/1.JEI.25.1.013009
History: Received August 11, 2015; Accepted December 11, 2015
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Abstract.  We describe an object classification method based on weighted score-level feature fusion using learned weights. Our method is able to recognize 20 object classes in a customized fruit dataset. Although the fusion of multiple features is commonly used to distinguish variable object classes, the optimal combination of features is not well defined. Moreover, in these methods, most parameters used for feature extraction are not optimized and the contribution of each feature to an individual class is not considered when determining the weight of the feature. Our algorithm relies on optimizing a single feature during feature selection and learning the weight of each feature for an individual class from the training data using a linear support vector machine before the features are linearly combined with the weights at the score level. The optimal single feature is selected using cross-validation. The optimal combination of features is explored and tested experimentally using a customized fruit dataset with 20 object classes and a variety of complex backgrounds. The experiment results show that the proposed feature fusion method outperforms four state-of-the-art fruit classification algorithms and improves the classification accuracy when compared with some state-of-the-art feature fusion methods.

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Citation

Hulin Kuang ; Leanne Lai Hang Chan ; Cairong Liu and Hong Yan
"Fruit classification based on weighted score-level feature fusion", J. Electron. Imaging. 25(1), 013009 (Jan 19, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.1.013009


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