基于流形模糊聚类算法的高光谱图像猪肉品质分类研究

Study on Hyperspectral Image Technology Based onManifold Fuzzy Clustering for Pork Quality Classification

中文关键词:  高光谱图像  模糊C均值聚类  等距映射  猪肉品质

英文关键词:hyperspectral image  fuzzy C-mean clustering algorithm  isometric mapping  pork quality

中文摘要:

      目的 以基于高光谱图像技术的冷鲜肉、解冻肉和变质肉的分类为研究对象, 针对特征维数过高的问题, 构建了一种基于流形模糊聚类算法的分类模型。方法 首先采用二维Gabor小波变换分别提取反应猪肉滴水损失、pH、颜色三种品质指标的14个特征波长下图像的8个纹理特征, 组成一个112维的特征变量作为猪肉品质的特征; 然后采用基于等距映射降维的模糊C均值聚类算法来构建猪肉品质分类模型。结果 通过猪肉品质分类实验得出, 二维Gabor小波变换能较好地提取猪肉的纹理特征; 与传统模糊C均值聚类算法相比, 基于等距映射降维的模糊C均值聚类算法能较好地解决高维样本聚类问题, 能准确地区分冷鲜肉、解冻肉和变质肉。结论 高光谱图像技术可应用于对猪肉品质分类。

英文摘要:

      Objective The classification of fresh chilled meat, frozen-thawed meat and spoiled meat based on the hyperspectral image technology as research object, and aiming at the problem of high feature dimension, a classification model based on manifold fuzzy clustering algorithm was established. Methods The 2D Gabor wavelet transform was used to extract the 8 texture features of image respectively under 14 characteristic wavelengths reflecting 3 kinds of quality index: drip loss, pH and color and thus forms a 112-dimensional feature variables as the feature of pork quality. Then the pork quality classification model was established through the fuzzy C-means clustering algorithm (FCM) based on Isometric Mapping (ISOMAP) dimensional reduction. Results The experiment of pork quality classification showed that 2D Gabor wavelet transform could effectively extract texture feature of pork. Compared with the traditional FCM clustering algorithm, FCM clustering algorithm based on ISOMAP dimensional reduction could better resolve the clustering problem of high dimensional samples and accurately distinguish the fresh chilled meat, frozen-thawed meat and spoiled meat. Conclusion The hyperspectral image technology could be used for pork quality classification.