结合LLE流形学习和支持向量机的猪肉颜色分级

猪肉颜色分级是肉品品质无损检测的重要内容。该文通过对猪肉彩色数字图像颜色规律的分析,结合猪肉红(R)、绿(G)、蓝(B)颜色分量及其亮度和饱和度值,构建了一个新的颜色维度。将采集的1070份猪肉图像样本进行专家分级和标记后,给出了猪肉颜色的特征表示,并利用支持向量机(SVM)进行了颜色分级。试验结果表明,随着训练集样本数的增加,分级准确率有所提高。当将所有样本作为训练集时,分级准确率高达96.5%。针对SVM分级后泛化能力不强的问题,采用流形学习LLE维数变换,使其泛化能力由37%提高近1倍。结果表明LLE可有效改善SVM的分级准确率。该方法可为猪肉品质无损检测的研究与应用提供参考。

Pork color grading based on LLE manifold learning and support vector machine

Pork color grading is an important task of nondestructive detection of meat quality. With analyzing the color feature of pork image and combining its color component value of red, green and blue and its luminance and saturation values converted by RGB color format, a new color feature value was created for the purpose of better expression of pork color. Then, 1070 collected pork samples were graded and marked by experts, and the color feature values of each pork image with special grade mark was obtained after image processing. A series of experiments were conducted to test the pork color grading accuracy rate based on support vector machine. The results showed that along with the increment of training sample number, the pork color grading accuracy rate increased. When all samples were selected as training set, the accuracy reached up to 96.5%. In order to improve the prediction accuracy of SVM (about 37%), locally liner embedding (LLE), a manifold learning method, was applied to reduce or increase the dimensions of pork color feature. The results showed that the prediction accuracy rate of the grading procedure based on LLE and SVM was nearly twice as high as that of the procedure only relying on SVM. Hence, combining the LLE manifold learning method and SVM, pork color grading accuracy will be improved. This method can provide a reference for study and application of nondestructive detection of pork quality.