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Median arc center corrected binary pattern (MACCBP) for noise robust feature extraction

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Abstract

Local binary pattern (LBP) is an efficient texture descriptor with increasing applications in machine vision. Notwithstanding the great ability of LBP in revealing texture features of natural images, this descriptor is sensitive to noise, and its accuracy is reduced when applied to noisy images. The two important noise sensitive components in computing LBP, which affect the generated binary patterns, are the central pixel value and the neighboring pixels values, which are used for thresholding. This paper proposes a noise robust texture descriptor that applies a novel mechanism for potential noisy central pixel detection and correction. Moreover, the proposed descriptor uses a new sampling method that corrects potential noisy neighboring pixels by replacing them with the median of their various radii neighboring pixels, which are located in a new arc-shaped structure. Since the proposed median arc center corrected binary pattern (MACCBP) uses pixels related to different radii patterns for code generation, both macro and micro structures participate in thresholding. Hence, performance is increased in both noisy and noiseless environments. Furthermore, the MACCBP applies the idea of completed LBP and extracts magnitude and center information in conjunction with the sign to achieve more noise robustness and classification accuracy. The proposed descriptor is extensively examined in noisy and noise-free experiments using Outex, UIUC, UMD and CUReT datasets. The experimental results show that MACCBP achieves high classification accuracy in the experiments with original images of the datasets, and when additive salt and pepper noise, Gaussian white noise and Gaussian blur are applied to the test images. The MACCBP is compared with its well-known state-of-the-art counterparts in terms of classification accuracy in the experiments. The results obtained from numerous and extensive tests demonstrate that the proposed descriptor is notably superior to its competitors in noisy and noiseless experiments.

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References

  • Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.

    Article  MATH  Google Scholar 

  • Alkhatib, M., & Hafiane, A. (2019). Robust adaptive median binary pattern for noisy texture classification and retrieval. IEEE Transactions on Image Processing, 28(11), 5407–5418.

    Article  MathSciNet  MATH  Google Scholar 

  • Bashar, F., Khan, A., Ahmed, F., & Kabir, M. H. (2014). Robust facial expression recognition based on median ternary pattern (mtp). In 2013 International Conference on Electrical Information and Communication technology (EICT), pp. 1–5. IEEE.

  • Bingham, E., Kaski, S., Laaksonen, J., & Lampinen, J. (2015). Advances in independent component analysis and learning machines. Academic Press.

  • Brahnam, S., Jain, L. C., Nanni, L., Lumini, A., et al. (2014). Local binary patterns: New variants and applications (Vol. 2). Springer.

    Book  MATH  Google Scholar 

  • Bruna, J., & Mallat, S. (2013). Invariant scattering convolution networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1872–1886.

    Article  Google Scholar 

  • Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015). Pcanet: A simple deep learning baseline for image classification? IEEE Transactions on Image Processing, 24(12), 5017–5032.

    Article  MathSciNet  MATH  Google Scholar 

  • Chaudhuri, B., Sarkar, N., & Kundu, P. (1993). Improved fractal geometry based texture segmentation technique. IEE Proceedings E (Computers and Digital Techniques), 140(5), 233–242.

    Article  Google Scholar 

  • Chen, J., Patel, V. M., Liu, L., Kellokumpu, V., Zhao, G., Pietikäinen, M., & Chellappa, R. (2017). Robust local features for remote face recognition. Image and Vision Computing, 64, 34–46.

    Article  Google Scholar 

  • Citraro, L., Mahmoodi, S., Darekar, A., & Vollmer, B. (2017). Extended three-dimensional rotation invariant local binary patterns. Image and Vision Computing, 62, 8–18.

    Article  Google Scholar 

  • Dana, K. J., Van Ginneken, B., Nayar, S. K., & Koenderink, J. J. (1999). Reflectance and texture of real-world surfaces. ACM Transactions on Graphics (TOG), 18(1), 1–34.

    Article  Google Scholar 

  • Ding, C., Choi, J., Tao, D., & Davis, L. S. (2015). Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 518–531.

    Article  Google Scholar 

  • Doshi, N. P., & Schaefer, G. (2012). A comprehensive benchmark of local binary pattern algorithms for texture retrieval. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 2760–2763. IEEE.

  • Fernández, A., Álvarez, M. X., & Bianconi, F. (2013). Texture description through histograms of equivalent patterns. Journal of mathematical imaging and vision, 45(1), 76–102.

    Article  MathSciNet  MATH  Google Scholar 

  • Guo, Y., Zhao, G., & PietikäInen, M. (2012). Discriminative features for texture description. Pattern Recognition, 45(10), 3834–3843.

    Article  Google Scholar 

  • Guo, Y., Zhao, G., Pietikäinen, M., & Xu, Z. (2010). Descriptor learning based on fisher separation criterion for texture classification. In Asian Conference on Computer Vision, pp. 185–198. Springer.

  • Guo, Z., Wang, X., Zhou, J., & You, J. (2015). Robust texture image representation by scale selective local binary patterns. IEEE Transactions on Image Processing, 25(2), 687–699.

    Article  MathSciNet  MATH  Google Scholar 

  • Guo, Z., Zhang, L., & Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19(6), 1657–1663.

    Article  MathSciNet  MATH  Google Scholar 

  • Hafiane, A., Seetharaman, G., & Zavidovique, B. (2007). Median binary pattern for textures classification. In International Conference Image Analysis and Recognition, pp. 387–398. Springer.

  • Heikkila, M., & Pietikainen, M. (2006). A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 657–662.

    Article  Google Scholar 

  • Heikkilä, M., Pietikäinen, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42(3), 425–436.

    Article  MATH  Google Scholar 

  • Huang, D., Ding, H., Wang, C., Wang, Y., Zhang, G., & Chen, L. (2014). Local circular patterns for multi-modal facial gender and ethnicity classification. Image and Vision Computing, 32(12), 1181–1193.

    Article  Google Scholar 

  • Jin, H., Liu, Q., Lu, H., & Tong, X. (2004). Face detection using improved lbp under bayesian framework. In Third International Conference on Image and Graphics (ICIG’04), pp. 306–309. IEEE.

  • Juefei-Xu, F., Naresh Boddeti, V., & Savvides, M. (2017). Local binary convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 19–28.

  • Kandaswamy, U., Schuckers, S. A., & Adjeroh, D. (2010). Comparison of texture analysis schemes under nonideal conditions. IEEE Transactions on Image Processing, 20(8), 2260–2275.

    Article  MathSciNet  MATH  Google Scholar 

  • Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097–1105.

  • Lazebnik, S., Schmid, C., & Ponce, J. (2005). A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1265–1278.

    Article  Google Scholar 

  • Liao, S., Law, M. W., & Chung, A. C. (2009). Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing, 18(5), 1107–1118.

    Article  MathSciNet  MATH  Google Scholar 

  • Lin, X., Zhao, C., & Pan, W. (2017). Towards accurate binary convolutional neural network. In Advances in Neural Information Processing Systems, pp. 345–353.

  • Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2019). From bow to cnn: Two decades of texture representation for texture classification. International Journal of Computer Vision, 127(1), 74–109.

    Article  Google Scholar 

  • Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017). Local binary features for texture classification: Taxonomy and experimental study. Pattern Recognition, 62, 135–160.

    Article  Google Scholar 

  • Liu, L., Lao, S., Fieguth, P. W., Guo, Y., Wang, X., & Pietikäinen, M. (2016). Median robust extended local binary pattern for texture classification. IEEE Transactions on Image Processing, 25(3), 1368–1381.

    Article  MathSciNet  MATH  Google Scholar 

  • Lu, J., Liong, V. E., & Zhou, J. (2017). Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(8), 1979–1993.

    Article  Google Scholar 

  • Maenpaa, T. I. (2004). The local binary pattern approach to texture analysis: extensions and applications. Ph.D. thesis, Oulu University.

  • Nanni, L., Brahnam, S., & Lumini, A. (2010). A local approach based on a local binary patterns variant texture descriptor for classifying pain states. Expert Systems with Applications, 37(12), 7888–7894.

    Article  Google Scholar 

  • Nanni, L., Lumini, A., & Brahnam, S. (2010). Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine, 49(2), 117–125.

    Article  MATH  Google Scholar 

  • Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllonen, J., & Huovinen, S. (2002). Outex-new framework for empirical evaluation of texture analysis algorithms. In Object recognition supported by user interaction for service robots, vol. 1, pp. 701–706. IEEE.

  • Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.

    Article  MATH  Google Scholar 

  • Pietikäinen, M., Hadid, A., Zhao, G., & Ahonen, T. (2011). Computer vision using local binary patterns (Vol. 40). Springer.

    Google Scholar 

  • Pietikäinen, M., & Zhao, G. (2015). Two decades of local binary patterns: A survey. In Advances in independent component analysis and learning machines, pp. 175–210. Elsevier.

  • Ryu, J., Hong, S., & Yang, H. S. (2015). Sorted consecutive local binary pattern for texture classification. IEEE Transactions on Image Processing, 24(7), 2254–2265.

    Article  MathSciNet  MATH  Google Scholar 

  • Shang, J., Chen, C., Pei, X., Liang, H., Tang, H., & Sarem, M. (2017). A novel local derivative quantized binary pattern for object recognition. The Visual Computer, 33(2), 221–233.

    Article  Google Scholar 

  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.

  • Song, K., Yan, Y., Zhao, Y., & Liu, C. (2015). Adjacent evaluation of local binary pattern for texture classification. Journal of Visual Communication and Image Representation, 33, 323–339.

    Article  Google Scholar 

  • Suryanarayana, S., Deekshatulu, B., Kishore, K. L., & Kumar, Y. R. (2012). Estimation and removal of Gaussian noise in digital images. International Journal of Electronics and Communication Engineering, 5(1), 23–33.

    Google Scholar 

  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.

  • Tabatabaei, S. M., & Chalechale, A. (2019). Local binary patterns for noise-tolerant semg classification. Signal, Image and Video Processing, 13(3), 491–498.

    Article  Google Scholar 

  • Tabatabaei, S. M., & Chalechale, A. (2020). Noise-tolerant texture feature extraction through directional thresholded local binary pattern. The Visual Computer, 36, 967–987.

    Article  Google Scholar 

  • Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635–1650.

    Article  MathSciNet  MATH  Google Scholar 

  • Tao, H., & Lu, X. (2019). Smoke vehicle detection based on robust codebook model and robust volume local binary count patterns. Image and Vision Computing, 86, 17–27.

    Article  Google Scholar 

  • Trefnỳ, J., & Matas, J. (2010). Extended set of local binary patterns for rapid object detection. In Computer vision winter workshop, pp. 1–7.

  • Varma, M., & Zisserman, A. (2008). A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2032–2047.

    Article  Google Scholar 

  • Xu, Y., Yang, X., Ling, H., & Ji, H. (2010). A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 161–168. IEEE.

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Acknowledgements

We would like to thank the authors of (Liu et al. 2016) and (Guo et al. 2015) for sharing their codes.

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  1. Computer Engineering Department, Razi University, Kermanshah, Iran

    Sayed Mohamad Tabatabaei & Abdolah Chalechale

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  1. Sayed Mohamad Tabatabaei
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Correspondence to Abdolah Chalechale.

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Tabatabaei, S.M., Chalechale, A. Median arc center corrected binary pattern (MACCBP) for noise robust feature extraction. Multidim Syst Sign Process 33, 1409–1444 (2022). https://doi.org/10.1007/s11045-022-00848-6

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