Hyperspectral imaging technique based on Geodesic K-medoids clustering and Gabor wavelets for pork quality evaluation

作     者：Zeng, Shan Chen, Ling Jiang, Liang Gao, Chongjun 

作者机构：Wuhan Polytech Univ Coll Math & Comp Sci Wuhan 430023 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING》 (Int. J. Wavelets Multiresolution Inf. Process.)

年 卷 期：2017年第15卷第6期

页      面：1750066-1750066页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Hubei National Natural Science Foundation (NSF) [2017CFB500] NSFC Jiangsu Science and Technology Program [BY2016009-03] 

主　　题：Hyperspectral image technology Gabor wavelet K-medoids clustering algorithm pork quality evaluation 

摘      要：This paper presents a pork quality evaluation method based on the hyperspectral image datasets of 96 pork samples in the range of 400-1000 nm. First, through the K-medoids clustering algorithm based on manifold distance, 30 important wavelengths are selected from 753 wavelengths, and final 8 optimum wavelengths are obtained based on the discriminant value and the spectral resolution. Then, the two-dimensional Gabor wavelet transform is used to extract the eight texture features of the image under the final eight wavelengths respectively, to form a 64-dimensional features of pork quality. Finally, using the fussy C-means (FCM) algorithm based on Isomap dimension reduction, the pork quality evaluation model is constructed. The result of wavelength extraction experiments show that although there is a strong linear correlation between adjacent bands in the hyperspectral image, there is an obvious nonlinear manifold relation in the whole band. Therefore, the K-medoids clustering algorithm based on manifold distance in this paper is more reasonable than the traditional principal component analysis (PCA) in characteristic wavelength selection. According to the experiment of pork quality evaluation, two-dimensional Gabor wavelet transform can extract the texture characteristics of pork better. Compared with the FCM algorithm based on PCA, the FCM algorithm based on Isomap can better solve the high-dimensional clustering problem, and can distinguish fresh chilled meat, frozen-thawed meat and spoiled meat accurately. The study shows that hyperspectral image technology can be used in pork quality evaluation.