Application of hybrid image features for fast and non-invasive classification of raisin

Journal of Food Engineering

Volumn 109, Issue 3, 2012, Pages 531-537

  Wang, Songjing ^a   Liu, Kangsheng ^a   Yu, Xinjie ^a   Wu, Di ^b   He, Yong ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

Color feature; Machine vision; Morphological feature; Raisin classification; Texture feature

Indexed keywords

CLASSIFICATION MODELS; COLOR AND TEXTURE FEATURES; COLOR FEATURES; FEATURE COMBINATION; FEATURE SETS; HYBRID IMAGE; LEAST SQUARES SUPPORT VECTOR MACHINES; LINEAR DISCRIMINANT ANALYSIS; MORPHOLOGICAL FEATURES; NON-INVASIVE; PARTIAL LEAST SQUARES; RADIAL BASIS FUNCTIONS; RGB IMAGES; SOFT INDEPENDENT MODELING OF CLASS ANALOGIES; TEXTURE FEATURE;

COMPUTER VISION; DISCRIMINANT ANALYSIS; IMAGE RETRIEVAL; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES; TEXTURES;

COLOR;

EID: 83955161140     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2011.10.028     Document Type: Article

References (34)

1. Anon, 1990. IEEE Standard 610.4-1990. In: IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology. IEEE, New York, USA.
2. D.A. Ballard, and C.M. Brown Computer Vision 1982 Prentice-Hall Englewood Cliffs, NJ, USA
3. A.I. Belousov, S.A. Verzakov, and F.J. Von A flexible classification approach with optimal generalisation performance: support vector machines Chemometrics and Intelligent Laboratory Systems 64 2002 15 25 (Pubitemid 35304556)
4. Q.S. Chen, J.W. Zhao, C.H. Fang, and D.M. Wang Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM) Spectrochimica Acta, Part A 66 3 2007 568 574 (Pubitemid 46199298)
5. Cui, Y.Y.; Zeng, Z.Y.; 2009. Remote sensing image classification based on the HSI transformation and fuzzy support vector machine. In: Proceedings of International Conference on Future Computer and Communication, Kuala Lumpar, Malaysia (CD-ROM).
6. C.J. Du, and D.W. Sun Recent development in the applications of image processing techniques for food quality evaluation Trends in Food Science and Technology 15 2004 230 249
7. C.J. Du, and D.W. Sun Correlating image texture features extracted by five different methods with the tenderness of cooked pork ham: a feasibility study Transactions of the ASABE 49 2 2006 441 448
8. K. Fukunaga Introduction to Statistical Pattern Recognition 1991 Academic Press New York, USA
9. R.C. Gonzalez, and R.E. Woods Digital Image Processing 1992 Addison-Wesley Publishing Co. Reading, MA
10. R. Haralick, K. Shanmugam, and I. Dinstein Textural features for image classification IEEE Transactions on Systems, Man and Cybernetics 3 1973 610 621
11. C.C. Huxsoll, H.R. Bolin, and B.E. Mackey Near-infrared analysis potential for grading raisin quality and moisture Journal of Food Science 60 1 1995 176 180
12. T.D. Lawrence On the meaning and use of kurtosis Psychological Methods 2 3 1996 292 307
13. X.L. Li, J. Yuan, T.X. Gu, and X.Y. Liu Level detection of raisins based on image analysis and neural network International Symposium on Neural Networks 6 2009 343 350
14. C. Lu, G.T. Van, J.A.K. Suykens, H.S. Van, I. Vergote, and D. Timmerman Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines Artificial Intelligent in Medicine 28 2003 281 306 (Pubitemid 36966127)
15. N.K. Okamura, M.J. Delwiche, and J.F. Thompson Raisin grading by machine vision Transactions of ASAE 36 2 1993 485 492 (Pubitemid 23685964)
16. M. Omid, M. Abbasgolipour, A. Keyhani, and S.S. Mohtasebi Implementation of an efficient image processing algorithm for grading raisins International Journal of Signal and Image Processing 1 2010 31 34
17. C.R. Rao The use and interpretation of principal component analysis in applied research Sankhya 26 1964 329 358
18. T. Satake, S. Chang, S. Omori, O. Fujioka, and O. Sakata Basic study on grading of Chinese dried green raisin using image information Journal of the Society of Agricultural Structures 33 4 2003 217 224
19. J.A.K. Suykens, and J. Vanderwalle Least squares support vector machine classifiers Neural Processing Letters 9 3 1999 293 300
20. J.A.K. Suykens, G.T. Van, B.J. De, M.B. De, and J. Vandewalle Least Squares Support Vector Machines 2002 World Scientific Publishing Singapore
21. J.L. Tang, X. Jing, and D.J. He Research of classification of raisin based on BP networks Journal of Agricultural Mechanization Research 11 2007 51 53
22. USDA, 2009. Raisins: World Markets and Trade. Available at: < http://usda.mannlib.cornell.edu/usda/fas/raiswm//2000s/2009/raiswm-09-04-2009. pdf > (accessed 11 March 2011).
23. S. Wold, and M. Sjostrom SIMCA: a method for analyzing chemical data in terms of similarity and analogy R. Bruce, B.R. Kowalski, Chemometrics Theory and Application 1977 American Chemical Society Washington 243 282
24. D. Wu, L. Feng, C. Zhang, and Y. He Early detection of Botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy Transactions of the ASABE 51 3 2008 1133 1139
25. D. Wu, Y. He, and S. Feng Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment Analytica Chimica Acta 610 2 2008 232 242
26. D. Wu, Y. He, S.J. Feng, and D.W. Sun Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM Journal of Food Engineering 84 1 2008 124 131 (Pubitemid 47189751)
27. D. Wu, H.Q. Yang, X.J. Chen, Y. He, and X.L. Li Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine Journal of Food Engineering 88 4 2008 474 483
28. D. Wu, X.J. Chen, and Y. He Application of multispectral image texture to discriminating tea categories based on DCT and LS-SVM Spectroscopy and Spectral Analysis 29 5 2009 1382 1385
29. D. Wu, X.J. Chen, P.Y. Shi, S.H. Wang, F.Q. Feng, and Y. He Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination Analytica Chimica Acta 634 2 2009 166 171
30. D. Wu, Y. He, J.H. Shi, and S.J. Feng Exploring near and mid infrared spectroscopy to predict trace iron and zinc contents in powdered milk Journal of Agricultural and Food Chemistry 57 5 2009 1697 1704
31. D. Wu, Y. He, P.C. Nie, F. Cao, and Y.D. Bao Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice Analytica Chimica Acta 659 1-2 2010 229 237
32. D. Wu, P.C. Nie, J. Cuello, Y. He, Z.P. Wang, and H.X. Wu Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder Journal of Food Engineering 102 3 2011 278 286
33. C.X. Zheng, D.W. Sun, and L.Y. Zheng Recent applications of image texture for evaluation of food qualities - a review Trends in Food Science and Technology 17 3 2006 113 128
34. L. Zhu Face recognition based on two-dimensional image principal component analysis Journal of Zhejiang University (Engineering Science) 41 2 2007 254 267