Fruit classification based on weighted score-level feature fusion

Hulin Kuang; Leanne L. H. Chan; Cairong Liu; Hong Yan

doi:10.1117/1.JEI.25.1.013009

19 January 2016 Fruit classification based on weighted score-level feature fusion

Hulin Kuang, Leanne L. H. Chan, Cairong Liu, Hong Yan

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 1, 013009 (January 2016). https://doi.org/10.1117/1.JEI.25.1.013009

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.

Citation Download Citation

Hulin Kuang, Leanne L. H. Chan, Cairong Liu, and Hong Yan "Fruit classification based on weighted score-level feature fusion," Journal of Electronic Imaging 25(1), 013009 (19 January 2016). https://doi.org/10.1117/1.JEI.25.1.013009

Published: 19 January 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available