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Color- and Gradient-Related Visual Dictionary Models for Discrimination of Glycine Max (L.) Merrill Quality

Glycine max (L.) Merrill is one of the most important economic and food crops in the world. Defective rate is an important indicator affecting Glycine max (L.) Merrill surface quality factors. In this study, we introduce two kinds of color- and gradient-related visual dictionary models for discrimination of Glycine max (L.) Merrill quality. The RGB color components is firstly transferred to L*a*b* color domain. The speeded-up robust features (SURF) is performed to obtain the local gradient-related characteristic representation. The KNN clustering method is individually performed to cluster the global L*a*b* color features and local gradient-related SURF features. The clustering centers can be viewed as visual words. All the visual words are collected to generate the visual dictionary. The visual clustering features are finally discriminated by the support vector machine algorithm. The result shows that the gradient-related model achieves higher discrimination accuracy than the color-related visual dictionary model.