Grading of Beef Marbling by Using Invariant Moments and An Improved Support Vector Machine
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Abstract:
The image processing techniques was used to improve the objectivity and degree of automation in the grading of beef marbling, in order to minimize the interference caused by human errors in the manual beef marbling grading process. This study proposed the use of a grading method for beef marbling that utilized the invariant moments, a gray level co-occurrence matrix, and a mixed kernel support vector machine (SVM), optimized by a chaotic bee colony. Firstly, the invariant moments and the statistical quantity of gray level co-occurrence matrix of the beef marbling image were computed in order to construct a feature vector. The training and testing samples of the beef marbling image were then inputted to a mixed kernel function SVM. Optimal recognition performance was attained by optimizing the penalty factor and kernel parameters of the mixed kernel function SVM using a chaotic bee colony algorithm. Finally, the samples to be graded were inputted to the SVM for classification and recognition, and the optimal grading results were obtained. A large number of experimental results revealed grading accuracies of 100% (Grade One), 93.3% (Grade Two), 93.3% (Grade Three), 96.7% (Grade Four), and 100% (Grade Five), based on the standard beef marbling image obtained by NY/T676-2010. The proposed method showed the highest grading accuracy compared to those of the methods developed utilizing gray moment and SVM, and the gray level co-occurrence matrix and the black propagation (BP) neural network; in addition, the obtained results were closest to the actual grading results obtained by the professional beef marbling grading division.
