A local binary pattern based texture descriptors for classification of tea leaves

Neurocomputing

Volumn 168, Issue , 2015, Pages 1011-1023

  Tang, Zhe ^a   Su, Yuancheng ^a   Er, Meng Joo ^b   Qi, Fang ^a   Zhang, Li ^c   Zhou, Jianyong ^c  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c Changsha Xiangfeng Tea Machinery Manufacturing Co   (China)

Author keywords

Gray level co occurrence matrix; Local binary pattern; Tea classification; Texture classification

Indexed keywords

BINS; EXTRACTION;

EFFECTIVE ALGORITHMS; GRAY LEVEL CO OCCURRENCE MATRIX(GLCM); GRAY LEVEL CO-OCCURRENCE MATRIX; LOCAL BINARY PATTERNS; PROCESSING PARAMETERS; TEA CLASSIFICATIONS; TEXTURE CLASSIFICATION; TEXTURE DESCRIPTORS;

TEXTURES;

ARTICLE; AUTOMATION; CAMELLIA SINENSIS; CLASSIFICATION ALGORITHM; CONTROL SYSTEM; DATA EXTRACTION; FOOD PROCESSING; GRAY LEVEL COOCCURRENCE MATRIX; IMAGE ANALYSIS; LOCAL BINARY PATTERN; PLANT LEAF; PRIORITY JOURNAL;

EID: 84937812245     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.05.024     Document Type: Article

References (33)

1. Wu D., Yang H., Chen X., He Y., Li X. Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine. J. Food Eng. 2008, 88:474-483.
2. Chen Q., Zhao J., Fang C.H., Wang D. Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2007, 66:568-574.
3. Borah S., Hines E.L., Bhuyan M. Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules. J. Food Eng. 2007, 79:629-639.
4. Gill G.S., Kumar A., Agarwal R. Nondestructive grading of black tea based on physical parameters by texture analysis. Biosyst. Eng. 2013, 116:198-204.
5. Borah S., Bhuyan M. Non-destructive testing of tea fermentation using image processing, insight-non-destructive testing and condition monitoring. J. Br. Inst. Nondestr. Test. 2003, 45:55-58.
6. Palacios-Morillo A., Alcázar Á., de Pablos F., Jurado J.M. Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques. Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2013, 103:79-83.
7. Laddi A., Sharma S., Kumar A., Kapur P. Classification of tea grains based upon image texture feature analysis under different illumination conditions. J. Food Eng. 2013, 115:226-231.
8. Li X., Nie P., Qiu Z.-J., He Y. Using wavelet transform and multi-class least square support vector machine in multi-spectral imaging classification of Chinese famous tea. Expert Syst. Appl. 2011, 38:11149-11159.
9. Chen Q., Zhao J., Cai J. Identification of tea varieties using computer vision. Trans. ASABE 2008, 51:623-628.
10. Li S., Kwok J.T., Zhu H., Wang Y. Texture classification using the support vector machines. Pattern Recognit. 2003, 36:2883-2893.
11. Liu H., Liu Y., Sun F. Traffic sign recognition using group sparse coding. Inf. Sci. 2014, 266:75-89.
12. Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24:971-987.
13. Yang B., Chen S. A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 2013, 120:365-379.
14. Shih F.Y., Chuang C.-F. Automatic extraction of head and face boundaries and facial features. Inf. Sci. 2004, 158:117-130.
15. Ahonen T., Hadid A., Pietikainen M. Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28:2037-2041.
16. Nanni L., Lumini A. RegionBoost learning for 2D+ 3D based face recognition. Pattern Recognit. Lett. 2007, 28:2063-2070.
17. Zhao G., Pietikainen M. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29:915-928.
18. O. Lahdenoja, M. Laiho, A. Paasio, Reducing the feature vector length in local binary pattern based face recognition, in: Image Processing, 2005. ICIP 2005. IEEE International Conference on, (IEEE2005), 2005, pp. II-914-917.
19. P. Paclík, R.P.W. Duin, G.M.P. Van Kempen, R. Kohlus, Supervised segmentation of textures in backscatter images, in: Pattern Recognition, 2002. Proceedings. 16th International Conference on, (IEEE2002), 2002, pp. 490-493.
20. Nanni L., Lumini A. Local binary patterns for a hybrid fingerprint matcher. Pattern Recognit. 2008, 41:3461-3466.
21. Nanni L., Lumini A., Brahnam S. Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 2010, 49:117-125.
22. Liao S., Law M.W.K., Chung A.C.S. Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 2009, 18:1107-1118.
23. Heikkilä M., Pietikäinen M., Schmid C. Description of interest regions with local binary patterns. Pattern Recognit. 2009, 42:425-436.
24. T. Ahonen, M. Pietikäinen, Soft histograms for local binary patterns, in: Proceedings of the Finnish Signal Processing Symposium, FINSIG, 2007, p. 1.
25. Zhao Y., Jia W., Hu R.-X., Min H. Completed robust local binary pattern for texture classification. Neurocomputing 2013, 106:68-76.
26. Mizukami Y., Sawai Y., Yamaguchi Y. Moisture content measurement of tea leaves by electrical impedance and capacitance. Biosyst. Eng. 2006, 93:293-299.
27. Shinskey F.G. Process Control Systems: Application, Design and Tuning 1990, McGraw-Hill, Inc.
28. Gill G.S., Kumar A., Agarwal R. Monitoring and grading of tea by computer vision-a review. J. Food Eng. 2011, 106:13-19.
29. Haralick R.M., Shanmugam K., Dinstein I.H. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 610-621.
30. Ulaby F.T., Kouyate F., Brisco B., Williams T.H.L. Textural information in SAR images. IEEE Trans. Geosci. Remote Sens. 1986, 24:235-245.
31. Hua B.O., Fu-Long M., Li-Cheng J. Research on computation of GLCM of image texture. Acta Electron. Sin. 2006, 1:155-158.
32. Ohanian P.P., Dubes R.C. Performance evaluation for four classes of textural features. Pattern Recognit. 1992, 25:819-833.
33. Clausi D.A., Yue B. Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery. IEEE Trans. Geosci. Remote Sens. 2004, 42:215-228.