Abstract
The surface texture of dried jujube fruits is a significant quality grading criterion. This paper introduced a novel visual feature fusion based on connected region density, texture features, and color features. The single-scale Two-Dimensional Discrete Wavelet Transform was used to perform single-scale decomposition and reconstruction of dried Hami jujube image before visual features extraction. The connected region density was extracted by the two different algorithms, whereas the texture features were extracted by Gray Level Co-occurrence Matrix and the color features were extracted by image processing algorithms. Based on selected features which obtained by correlation analysis of visual features, the accuracy rate of the optimized Support Vector Machine classification model was 96.67%. In comparing with Extreme Learning Machine classification model and other fusion methods, the optimized Support Vector Machine based on selected visual features fusion was better.
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References
Ajay G, Adrian B. Parameterized principal component analysis. Pattern Recogn. 78: 215-227 (2018)
Beura S, Majhi B, Dash R. Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing. 154: 1-14 (2015)
Booth HS, Maindonald JH, Wilson SR, Gready JE. An efficient Z-score algorithm for assessing sequence alignments. J. Comput Biol. 11: 616-625 (2004)
China Standard. Product of geographical indications Hami big jujubes (DB65/T 3460-2012). Available from: http://www.zbgb.org/50/standardDetail2306572.htm. Accessed Dec. 21, 2017.
Clausi DA. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28: 45-62 (2002)
Dai YP, Wang YT, Xue JR, Liu XN, Liu BH, Guo XY. Research of segmentation method on image of Lingwu Long Jujubes based on a new extraction model of Hue. IEEE Sens. J. 17: 6029-6036 (2017)
Guttormsen E, Toldnes B, Bondø M, Eilertsen A, Gravdahl J, Mathiassen, J. A machine vision system for robust sorting of herring fractions. Food Bioprocess. Tech. 9: 1893-1900 (2016)
Hastie T, Tibshirani R. Classification by pair wise coupling. Ann. Appl Stat. 26: 451-471 (1998)
Huang GB. What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cogn Comput. 7: 263-278 (2015)
Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing. 70: 489-501(2006)
Jang KH, Choi JL, Yoo TK, Kwon MK, Kim DW. Survival prediction of rats with hemorrhagic shocks using support vector machine. J. Biomed. Eng. Res. 33: 1-7 (2012)
Jia WK, Zhao Dean, Ding L. An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample. Appl. Soft Comput. 48: 373-384 (2016)
Lloyd K, Rosin PL, Marshall D, Moore SC. Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measures. Mach. Vis. Appl. 28: 361-371 (2017)
Li JW, Ding SD, Ding, XL. Comparison of antioxidant capacities of extractes from five cultivars of Chinese jujubes. Process. Biochem. 40: 3607-3613 (2005)
Lin D, Zhang A, Gu J, Chen X, Wang Q, Yang L, Chou Y, Liu G, Wang J. Detection of multipoint pulse waves and dynamic 3D pulse shape of the radial artery based on binocular vision theory. Comput. Meth. Programs Biomed. 2018: 61-73 (2018)
Manickavasagan A, Al-Mezeini NK, Al-Shekaili HN. RGB color imaging technique for grading of dates. Sci. Hortic Amsterdam. 175: 87-94 (2014)
Muhammad G. Date fruits classification using texture descriptors and shape-size features. Eng. Appl. Artif. Intell. 37: 361-367(2015)
Ma BX, Qi XX, Wang LL, Zhu RG, Chen QG, Li FX, Wang WX. Size and defect detection of Hami Big Jujubes based on computer vision. pp. 750-754. In: Advanced Materials Research. August 30, Jinan Hotel, Shandong, China. Translation Technology Publications, Changsha, China (2012)
Pu YF, Ding T, W WJ, Xiang YJ, Ye XQ, Li M, Liu DH. Effect of harvest, drying and storage on the bitterness, moisture, sugars, free amino acids and phenolic compounds of jujube fruit (Zizyphus jujuba cv. Junzao). J. Sci. Food Agric. 98: 628-634 (2018)
Vapnik V, Cortes C. Support vector networks. Mach Learn. 20: 273-297 (1995)
Wu LG, He JG, Liu GS, Wang SL, He XG. Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging. Postharvest. Biol. Tecnol. 112: 134-142 (2016)
Yuan Q, Zhou W, Li S, Cai D. Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy. Res. 96:29-38 (2011)
Zhang JX, Ma QQ, Li W, Xiao TT. Feature extraction of jujube fruit wrinkle based on the watershed segmentation. Int. J. Agric. Biol. Eng. 10: 165-172 (2017)
Acknowledgements
This work was supported by the Natural Science Foundation of China (Grant Number 61763043) and the National Key Technologies R&D Program of China (Grant Numbers 2015BAD19B03).
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Luo, X., Ma, B., Wang, W. et al. Evaluation of surface texture of dried Hami Jujube using optimized support vector machine based on visual features fusion. Food Sci Biotechnol 29, 493–502 (2020). https://doi.org/10.1007/s10068-019-00683-9
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DOI: https://doi.org/10.1007/s10068-019-00683-9