Abstract
This work performs dimensionality reduction-based classification on fleece fabric-based images taken by a thermal camera. In order to convert images into the gray level, a principal component analysis-based dimension reduction stage was proposed. In addition, symmetric central local binary patterns were performed with the help of the proposed method by using the images after dimension reduction process. The local binary pattern features preserve local texture features from different kinds of defective image types. The experimental results showed that combined work has a great classification accuracy. The classification accuracy was reported using two different algorithms: Naive Bayes and K-nearest neighbor classifier.
Similar content being viewed by others
References
Karayiannis, Y., Stojanovic, R., Mitropoulos, P., Koulamas, C., Stouraitis, T., Koubias, S. and Papadopoulos, G.: Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks. In: Proceedings of the 6th IEEE International Conference on Electronics, Circuits and Systems, pp. 765–768, (1999)
Stehling, R.O.A., Nascimento, M. and Falcão, A.X.: A compact and efficient image retrieval approach based on border/interior pixel classification. In: International Conference on Information and Knowledge Management, pp. 102–109, (2002)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J. and Zabih, R.: Image indexing using color correlograms. In: Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–768, (1997)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Mak, K.L., Peng, P.: Detecting defects in textile fabrics with optimal Gabor filters. Int. J. Comput. Sci. 1(4), 274–282 (2006)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Demir, Ö., Yılmaz Çamurcu, A.: Computer-aided detection of lung nodules using outer surface features. Bio-Med. Mater. Eng. 26(s1), 1213–1222 (2015)
Çelik, H., Dülger, L., Topalbekiroğlu, M.: Development of a machine vision system: real-time fabric defect detection and classification with neural networks. J. Text. Inst. 105(6), 575–585 (2014)
Ponti, M., Nazaré, T.S., Thumé, G.S.: Image quantization as a dimensionality reduction procedure in color and texture feature extraction. Neurocomputing 173, 385–396 (2016)
Hu, M., Tsai, I.: Fabric inspection based on best wavelet packet bases. Text. Res. J. 70(8), 662–670 (2000)
Jianli, L., Baoqi, Z.: Identification of fabric defects based on discrete wavelet transform and back-propagation neural network. J. Text. Inst. 98(4), 355–362 (2007)
Kuo, C.-F.J., Su, T.-L.: Gray relational analysis for recognizing fabric defects. Text. Res. J. 73(5), 461–465 (2003)
Yıldız, K., Buldu, A., Demetgul, M.: A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm. J. Ind. Text. 45(5), 780–795 (2016)
Gade, R., Moeslund, T.B.: Thermal cameras and applications: a survey. Mach. Vis. Appl. 25(1), 245–262 (2014)
Jolliffe, I.: Principal Component Analysis, in Wiley StatsRef: Statistics Reference Online. Wiley, New Jersey (2014)
López, M., Ramírez, J., Górriz, J., Salas-Gonzalez, D., Álvarez, I., Segovia, F., Puntonet, C.: Automatic tool for Alzheimer’s disease diagnosis using PCA and Bayesian classification rules. Electron. Lett. 45(8), 389–391 (2009)
Andersen, A.H., Gash, D.M., Avison, M.J.: Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. Magn. Reson. Imaging 17(6), 795–815 (1999)
Yoon, U., Lee, J.-M., Im, K., Shin, Y.-W., Cho, B.H., Kim, I.Y., Kwon, J.S., Kim, S.I.: Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. Neuroimage 34(4), 1405–1415 (2007)
López, M., Ramírez, J., Górriz, J., Álvarez, I., Salas-Gonzalez, D., Segovia, F., Chaves, R.: SVM-based CAD system for early detection of the Alzheimer’s disease using kernel PCA and LDA. Neurosci. Lett. 464(3), 233–238 (2009)
Kaymak, S.: Face detection, recognition and reconstruction using eigenfaces. EE574 Detection and Estimation Theory. 4 (2003)
Ismail, I.A., Ramadan, M.A., El Danf, T. and Samak, A.H.: Automatic signature recognition and verification using principal components analysis. In: 5th International Conference on Computer Graphics, Imaging and Visualisation,(CGIV’08) pp. 356–361, (2008)
Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice hall, Upper Saddle River (2002)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)
Mäenpää, T., Pietikäinen, M.: Texture analysis with local binary patterns. Handb. Pattern Recognit. Comput. Vis. 3, 197–216 (2005)
Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)
Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit. 43(3), 706–719 (2010)
Zhao, G., Ahonen, T., Matas, J., Pietikäinen, M.: Rotation-invariant image and video description with local binary pattern features. IEEE Trans. Image Process. 21(4), 1465–1477 (2012)
Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image Vis. Comput. 30(2), 86–99 (2012)
Li, W. and Fritz, M.: Recognizing materials from virtual examples, in Computer Vision—ECCV 2012. Springer, pp. 345–358. (2012)
Qi, X., Xiao, R., Li, C.-G., Qiao, Y., Guo, J., Tang, X.: Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2199–2213 (2014)
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Computer Vision Using Local Binary Patterns. pp. 13–47. Springer, London (2011)
Qi, X., Shen, L., Zhao, G., Li, Q., Pietikäinen, M.: Globally rotation invariant multi-scale co-occurrence local binary pattern. Image and Vision Computing. 43, 16–26 (2015)
Verma, M., Raman, B.: Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. J. Vis. Commun. Image Represent. 32, 224–236 (2015)
Xia, M., Lu, W., Yang, J., Ma, Y., Yao, W., Zheng, Z.: A hybrid method based on extreme learning machine and k-nearest neighbor for cloud classification of ground-based visible cloud image. Neurocomputing 160, 238–249 (2015)
Zhou, X., Wang, S., Xu, W., Ji, G., Phillips, P., Sun, P., Zhang, Y.: Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier. In: Bioinformatics and Biomedical Engineering. pp. 201–209. Springer, Switzerland (2015)
Sheppard, S., Lawson, N.D., Zhu, L.J.: Accurate identification of polyadenylation sites from 3’end deep sequencing using a Naïve Bayes classifier. Bioinformatics 29(20), 2564–2571 (2013)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yildiz, K. Dimensionality reduction-based feature extraction and classification on fleece fabric images. SIViP 11, 317–323 (2017). https://doi.org/10.1007/s11760-016-0939-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-016-0939-9