Application of hybrid image features for fast and non-invasive classification of raisin

Abstract

A new approach for the non-invasive classification of raisins is presented based on the hybrid image features, namely morphological, color and texture features. A total of 74 features (8 morphological, 30 color, and 36 textural) were extracted from RBG images. Seven kinds of models were established based on different feature sets. They were three kinds of models established based on single feature set, three kinds of models established based on the combination of two feature sets, and one kind of model established based on the combination of all feature sets. Five kinds of classifiers, namely partial least squares (PLS), linear discriminant analysis (LDA), soft independent modeling of class analogy (SIMCA), and least squares support vector machine (LS-SVM) with linear and radial basis function (RBF) kernels were used for the model establishment based on different feature sets. The best correct answer rates (CAR) of 99% was obtained when LDA was used to establish the classification model based on the combination of all feature sets, which was higher than those of the models established based on single feature set or the combination of two feature sets. The results show that the feature combination is helpful to improve the accuracy of raisin classification. It was concluded that the varieties of raisin could be accurately classified based on RGB image features and the combination of morphological, color and texture features was an accurate way to improve the accuracy of classification.