• Fauziah Kasmin Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • Azizi Abdullah Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
  • Anton Satria Prabuwono Faculty of Computing and Information Technology Rabigh, King Abdul Aziz University, Saudi Arabia




Binarization, local neighbourhood, ensemble, weights, QR decomposition method


Supervised binarization is a method that learn pre-classified data in order to classify a particular pixel whether it is belong to a foreground or a background. The performance of supervised approach is usually better than that of unsupervised ones since it is designed to use classification criteria determined by ground truth data. By using this approach, orientations of local neighbourhood grey level information that are based on eight orientations have been developed to characterize a particular pixel. These orientations are combined together since it may reduce the risk of making a particular poor selection of these orientations. In order to ensemble all orientations, heuristic method have been used to determine weights for each orientation. However, determination of weights using heuristic method is not efficient and not enough as it provides incomplete information. Furthermore, these orientations might be influenced by other different factors. This will lead to wrongly assigning weights to a particular orientation. Hence, determination of weights to combine eight orientations to characterize a particular pixel by using QR decomposition method is proposed. By using QR decomposition method, computational complexity is low and weights obtained for each orientation are optimal. In order to test the proposed approach, 21 document images from DIBCO2009 and DIBCO2011 databases and 55 retinal images from DRIVE and STARE databases have been used. The results of the proposed method clearly show significant improvement where higher average accuracy is obtained compared to by using heuristic method.   


Author Biographies

  • Fauziah Kasmin, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
    Computing Industry Department
  • Azizi Abdullah, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
    Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology
  • Anton Satria Prabuwono, Faculty of Computing and Information Technology Rabigh, King Abdul Aziz University, Saudi Arabia
    Faculty of Computing and Information Technology Rabigh


LaTorre, A., Alonso-Nanclares, L., Muelas, S., Peña, J. M. and DeFelipe, J. 2013. Segmentation Of Neuronal Nuclei Based On Clump Splitting And A Two-Step Binarization Of Images. Expert Syst. Appl. 40(16): 6521-6530.

Ahmadi, E., Azimifar, Z., Shams, M., Famouri, M., and Shafiee, M., J. 2015. Document Image Binarization Using A Discriminative Structural Classifier. Pattern Recognit. Lett. 63: 36-42.

Xie, S., Li, Y., Jia, Z., and Ju, L. 2013. Binarization Based Implementation For Real-Time Human Detection. 23rd Int. Conf. F. Program. Log. Appl. 1-4.

Lin, Z., and Yu, H. 2011. The Pupil Location Based on the OTSU Method and Hough Transform. Procedia Environ. Sci. 8: 352-356.

Ng, H., F. 2006. Automatic Thresholding For Defect Detection. Pattern Recognit. Lett. 27(14): 1644-1649.

Chaki, N., Shaikh, S., H., and Saeed, K. 2014. A Comprehensive Survey on Image Binarization Techniques. Explor. Image Bin. Tech. Stud. Comput. Intell. 560: 5-16.

Chamchong, R. 2010. A Review of Evaluation of Optimal Binarization Technique for Character Segmentation in Historical Manuscripts. 2010 Third Int. Conf. Knowl. Discov. Data Min. 236-240.

Muhlich, M., Friedrich, D., and Aach, T. 2012. Design and Implementation of Multisteerable Matched Filters. IEEE Trans. Pattern Anal. Mach. Intell. 34(2): 279-291.

Wilson, R., Knutsson, H., and Granlund, G. 1983. Anisotropic Nonstationary Image Estimation and Its Applications: Part II--Predictive Image Coding. IEEE Trans. Commun. 31(3): 388-397.

Ha, J., C. 2011. Real-Time Visual Tracking Using Image Processing and Filtering Methods. Proquest Umi Dissertation Publishing.

Merigó, J., M., Casanovas, M., and Yang, J., B. 2014. Group Decision Making With Expertons And Uncertain Generalized Probabilistic Weighted Aggregation Operators. Eur. J. Oper. Res. 235: 215-224.

Bin Yan, H., Huynh, V., N., Nakamori, Y., and Murai, T. 2011. On Prioritized Weighted Aggregation In Multi-Criteria Decision Making. Expert Syst. Appl. 38(1): 812-823.

Naderahmadian, Y., and Hosseini-Khayat, S. 2010. Fast Watermarking Based On QR Decomposition In Wavelet Domain. Proc. - 2010 6th Int. Conf. Intell. Inf. Hiding Multimed. Signal Process. IIHMSP 2010. 127-130.

Zhou, S., and Shi, J. 2004. Identification Of Non-Linear Effects In Rotor Systems Using Recursive QR Factorization Method. J. Sound Vib. 270(1-2): 455-469.

Yam, J., Y., F., and Chow, T., W., S. 2000. A Weight Initialization Method For Improving Training Speed In Feedforward Neural Network. Neurocomputing. 30: 219-232.

Li, Z., Liu, G., Xu, Y., and Cheng, Y. 2014. Modified Directional Weighted Filter For Removal Of Salt & Pepper Noise. Pattern Recognit. Lett. 40: 113-120.

Li, N., Huo, H., Zhao, Y., M., Chen, X., and Fang, T. 2013. A Spatial Clustering Method With Edge Weighting For Image Segmentation. IEEE Geosci. Remote Sens. Lett. 10(5): 1124-1128.

Riaz, M., M., and Ghafoor, A. 2012. QR Decomposition based Image Enhancement for Through Wall Imaging. IEEE Conference Proceedings. 978-983.

Aqil, M., Hong, K., Jeong, M., Y., and Ge, S., S. 2012. Online Brain Imaging by QR Factorization of Normalized Regressor Functions. Proceedings of 2012 IEEE International Conference on Machatronics and Automation. 1370-1374.

Amintoosi, M., Farzam, F., Fathy, M., Analoui, M., and Mozayani, N. 2007. QR Decomposition-Based Algorithm for Background Subtraction. IEEE Conference Proceedings. 1093-1096.

Golub, G., H., and Van Loan, C., F. 1996. Matrix Computations. The John Hopkins University Press.

Tuntas, R. 2014. The Modelling And Analysis Of Nonlinear Systems Using A New Expert System Approach. Iran. J. Sci. Technol. A. 3: 365-372.

Ibrahim R., W., and Jalab, H., A. 2015. Image Denoising Based On Approximate Solution Of Fractional Cauchy-Euler Equation By Using Complex-Step Method. Iran. J. Sci. Technol. A. 243-251.

Hsu, C., Chang, C., and Lin, C. 2010. A Practical Guide to Support Vector Classification.

Gatos, B., Ntirogiannis, K., and Pratikakis, I. 2009. ICDAR 2009 Document Image Binarization Contest (DIBCO 2009). 10th International Conference on Document Analysis and Recognition. 1375-1382.

Pratikakis, I., Gatos, B., and Ntirogiannis, K. 2011. ICDAR 2011 Document Image Binarization Contest (DIBCO 2011). International Conference on Document Analysis and Recognition. 1506-1510.

Staal, J., Abrà moff, M. D., Niemeijer, M., Viergever,M., A., and Van Ginneken, B. 2004. Ridge-based Vessel Segmentation in Color Images of the Retina. IEEE Trans. Med. Imaging. 23(4): 501-509.

Hoover, A., Kouznetsova, V. and Goldbaum, M. 2000. Locating Blood Vessels In Retinal Images By Piecewise Threshold Probing Of A Matched Filter Response. IEEE Trans. Med. Imaging. 19(3): 203-10.

Yasnoff, W., A., Mui, J., K., and Bacus, J., W. 1977. Error Measures For Scene Segmentation. Pattern Recognit. 9(4): 217-231.

Polikar, R. 2006. Ensemble Based Systems in Decison Making. IEEE Circuits Syst. Mag. 21-45.

Kittler, J., and Illingworth, J. 1986. Minimum Error Thresholding. Pattern Recognit. 19(1): 41-47.

Otsu, N. 1979. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man, Cybern. SMC-9(1): 62-66.

Niblack, W. 1985. An Introduction to Digital Image Processing. Strandberg Publishing Company.

Cheriet, M., Farrahi Moghaddam, R., and Hedjam, R. 2013. A Learning Framework For The Optimization And Automation Of Document Binarization Methods. Comput. Vis. Image Underst. 117(3): 269-280.

Liu, C., Tsai, C., Liu, J., Yu, C., and Yu, S. 2012. Pectoral Muscle Segmentation Algorithm For Digital Mammograms Using Otsu Thresholding And Multiple Regression Analysis. Comput. Math. with Appl. 64(5): 1100-1107.

Yang, J., Tseng, J., C., and Tseng, P., S. 2015. Path Planning On Satellite Images For Unmanned Surface Vehicles. Int. J. Nav. Archit. Ocean Eng. 7(1): 87-99.






Science and Engineering

How to Cite

WEIGHT DETERMINATION FOR SUPERVISED BINARIZATION ALGORITHM BASED ON QR DECOMPOSITION. (2017). Jurnal Teknologi, 79(2). https://doi.org/10.11113/jt.v79.7185