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A Novel Texture Classification Method Based on Neutrosophic Truth

Year 2020, Volume: 3 Issue: 1, 28 - 39, 30.04.2020
https://doi.org/10.35377/saucis.03.01.709186

Abstract

Texture analysis is one of the basic procedures used in solving problems in computer vision and image processing. In this study, we propose a new local binary pattern (LBP) method based on neutrosophic set. The proposed method is named as the NZ - LBP. In the proposed NZ - LBP method, the texture image is converted into a neutrosophic set and the texture image is expressed by truth membership set. The local binary pattern features are calculated, by using the neutrosophic truth set instead of the original input image. The neutrosophic membership sets are more resistant to noise than the original input image. The neutrosophic set suppresses noise components, so that edge information can be calculated more accurately. Thus, utilization of the neutrosophic truth set instead of the original image has provided more effective local binary pattern features. The proposed method is able to achieve high classification accuracy with low feature size, reasonable computational cost. Experimental results show that the proposed method increases the accuracy of the local binary pattern method to the classification by approximately 11% without increasing the feature dimension. The obtained results reveal that the proposed method is applicable for real-time applications.

References

  • [1] G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto, “Dynamic Textures,” Int. J. Comput. Vis., vol. 51, no. 2, pp. 91–109, 2003, doi: 10.1023/A:1021669406132.
  • [2] M. A. Muqeet and R. S. Holambe, “Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition,” Appl. Comput. Informatics, vol. 15, no. 2, pp. 163–171, Jul. 2019, doi: 10.1016/J.ACI.2017.11.002.
  • [3] M. Varma and A. Zisserman, “A Statistical Approach to Texture Classification from Single Images,” Int. J. Comput. Vis., vol. 62, no. 1/2, pp. 61–81, Apr. 2005, doi: 10.1023/B:VISI.0000046589.39864.ee.
  • [4] P. P. Ohanian and R. C. Dubes, “Performance evaluation for four classes of textural features,” Pattern Recognit., vol. 25, no. 8, pp. 819–833, Aug. 1992, doi: 10.1016/0031-3203(92)90036-I.
  • [5] A. Speis and G. Healey, “Feature extraction for texture discrimination via random field models with random spatial interaction,” IEEE Trans. Image Process., vol. 5, no. 4, pp. 635–645, Apr. 1996, doi: 10.1109/83.491339.
  • [6] W.-K. Lam and C.-K. Li, “Rotated texture classification by improved iterative morphological decomposition,” IEE Proc. - Vision, Image, Signal Process., vol. 144, no. 3, p. 171, 1997, doi: 10.1049/ip-vis:19971198.
  • [7] T. Randen and J. H. Husoy, “Filtering for texture classification: a comparative study,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 4, pp. 291–310, Apr. 1999, doi: 10.1109/34.761261.
  • [8] N. Dasgupta and L. Carin, “Texture analysis with variational hidden Markov trees,” IEEE Trans. Signal Process., vol. 54, no. 6, pp. 2353–2356, Jun. 2006, doi: 10.1109/TSP.2006.872588.
  • [9] A. Maleki, B. Rajaei, and H. R. Pourreza, “Rate-Distortion Analysis of Directional Wavelets,” IEEE Trans. Image Process., vol. 21, no. 2, pp. 588–600, Feb. 2012, doi: 10.1109/TIP.2011.2165551.
  • [10] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Trans. Syst. Man. Cybern., vol. SMC-3, no. 6, pp. 610–621, Nov. 1973, doi: 10.1109/TSMC.1973.4309314.
  • [11] G. R. Cross and A. K. Jain, “Markov Random Field Texture Models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-5, no. 1, pp. 25–39, Jan. 1983, doi: 10.1109/TPAMI.1983.4767341.
  • [12] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002, doi: 10.1109/TPAMI.2002.1017623.
  • [13] P. Subudhi and S. Mukhopadhyay, “An efficient graph reduction framework for interactive texture segmentation,” Signal Process. Image Commun., vol. 74, pp. 42–53, May 2019, doi: 10.1016/J.IMAGE.2019.01.010.
  • [14] C. Li, Y. Huang, X. Yang, and H. Chen, “Marginal distribution covariance model in the multiple wavelet domain for texture representation,” Pattern Recognit., vol. 92, pp. 246–257, Aug. 2019, doi: 10.1016/J.PATCOG.2019.04.003.
  • [15] C. Li, Y. Huang, and L. Zhu, “Color texture image retrieval based on Gaussian copula models of Gabor wavelets,” Pattern Recognit., vol. 64, pp. 118–129, Apr. 2017, doi: 10.1016/J.PATCOG.2016.10.030.
  • [16] Y. Song et al., “Gaussian derivative models and ensemble extreme learning machine for texture image classification,” Neurocomputing, vol. 277, pp. 53–64, Feb. 2018, doi: 10.1016/J.NEUCOM.2017.01.113.
  • [17] K. Hanbay, N. Alpaslan, M. F. Talu, and D. Hanbay, “Principal curvatures based rotation invariant algorithms for efficient texture classification,” Neurocomputing, vol. 199, pp. 77–89, Jul. 2016, doi: 10.1016/j.neucom.2016.03.032.
  • [18] K. Hanbay, N. Alpaslan, M. F. Talu, D. Hanbay, A. Karci, and A. F. Kocamaz, “Continuous rotation invariant features for gradient-based texture classification,” Comput. Vis. Image Underst., vol. 132, pp. 87–101, 2015, doi: 10.1016/j.cviu.2014.10.004.
  • [19] X. Bu, Y. Wu, Z. Gao, and Y. Jia, “Deep convolutional network with locality and sparsity constraints for texture classification,” Pattern Recognit., vol. 91, pp. 34–46, Jul. 2019, doi: 10.1016/J.PATCOG.2019.02.003.
  • [20] S. Basu et al., “Deep neural networks for texture classification—A theoretical analysis,” Neural Networks, vol. 97, pp. 173–182, Jan. 2018, doi: 10.1016/J.NEUNET.2017.10.001.
  • [21] J. Zhang, Y. Xie, Q. Wu, and Y. Xia, “Medical image classification using synergic deep learning,” Med. Image Anal., vol. 54, pp. 10–19, May 2019, doi: 10.1016/J.MEDIA.2019.02.010.
  • [22] M. Talo, U. B. Baloglu, Ö. Yıldırım, and U. Rajendra Acharya, “Application of deep transfer learning for automated brain abnormality classification using MR images,” Cogn. Syst. Res., vol. 54, pp. 176–188, May 2019, doi: 10.1016/J.COGSYS.2018.12.007.
  • [23] G. Srivastava and R. Srivastava, “Salient object detection using background subtraction, Gabor filters, objectness and minimum directional backgroundness,” J. Vis. Commun. Image Represent., vol. 62, pp. 330–339, Jul. 2019, doi: 10.1016/J.JVCIR.2019.06.005.
  • [24] X. Zhao, Y. Lin, and J. Heikkila, “Dynamic Texture Recognition Using Volume Local Binary Count Patterns With an Application to 2D Face Spoofing Detection,” IEEE Trans. Multimed., vol. 20, no. 3, pp. 552–566, Mar. 2018, doi: 10.1109/TMM.2017.2750415.
  • [25] Z. Pan, Z. Li, H. Fan, and X. Wu, “Feature based local binary pattern for rotation invariant texture classification,” Expert Syst. Appl., vol. 88, pp. 238–248, Dec. 2017, doi: 10.1016/J.ESWA.2017.07.007.
  • [26] N. Alpaslan and K. Hanbay, “Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification,” IEEE Access, vol. 8, pp. 54415–54430, 2020, doi: 10.1109/ACCESS.2020.2981720.
  • [27] F. Yuan, X. Xia, and J. Shi, “Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns,” Inf. Sci. (Ny)., vol. 460–461, pp. 202–222, Sep. 2018, doi: 10.1016/J.INS.2018.05.033.
  • [28] S. Naeem, F. Riaz, A. Hassan, and R. Nisar, “Description of Visual Content in Dermoscopy Images Using Joint Histogram of Multiresolution Local Binary Patterns and Local Contrast,” in International Conference on Intelligent Data Engineering and Automated Learning, 2015, pp. 433–440, doi: 10.1007/978-3-319-24834-9_50.
  • [29] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of Interest Regions with Center-Symmetric Local Binary Patterns,” in Computer Vision, Graphics and Image Processing, 2006, pp. 58–69, doi: 10.1007/11949619_6.
  • [30] Y. El merabet and Y. Ruichek, “Local Concave-and-Convex Micro-Structure Patterns for texture classification,” Pattern Recognit., vol. 76, pp. 303–322, Apr. 2018, doi: 10.1016/J.PATCOG.2017.11.005.
  • [31] F. Smarandache, A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability, Third edit. American Research Press, 2003.
  • [32] K. Hanbay, “Nötrozofik Küme Temelli Difüzyon Metodu Kullanılarak Görüntülerdeki Örtüşme Problemini Azaltma,” Eur. J. Sci. Technol., no. 18, pp. 505–514, Apr. 2020, doi: 10.31590/ejosat.695191.
  • [33] J. Mohan, V. Krishnaveni, and Y. Guo, “MRI denoising using nonlocal neutrosophic set approach of Wiener filtering,” Biomed. Signal Process. Control, vol. 8, no. 6, pp. 779–791, Nov. 2013, doi: 10.1016/j.bspc.2013.07.005.
  • [34] T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen, and S. Huovinen, “Outex - New framework for empirical evaluation of texture analysis algorithms,” Proc. - Int. Conf. Pattern Recognit., vol. 16, no. 1, pp. 701–706, 2002, doi: 10.1109/icpr.2002.1044854.
  • [35] Y. Xu, H. Ji, and C. Fermüller, “Viewpoint invariant texture description using fractal analysis,” Int. J. Comput. Vis., vol. 83, no. 1, pp. 85–100, Jun. 2009, doi: 10.1007/s11263-009-0220-6.
  • [36] A. Pillai, R. Soundrapandiyan, S. Satapathy, S. C. Satapathy, K. H. Jung, and R. Krishnan, “Local diagonal extrema number pattern: A new feature descriptor for face recognition,” Futur. Gener. Comput. Syst., vol. 81, pp. 297–306, Apr. 2018, doi: 10.1016/j.future.2017.09.055.
  • [37] S. R. Dubey, S. K. Singh, and R. K. Singh, “Local Bit-Plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 4, pp. 1139–1147, Jul. 2016, doi: 10.1109/JBHI.2015.2437396.
  • [38] Y. Kaya, Ö. F. Ertuğrul, and R. Tekin, “Two novel local binary pattern descriptors for texture analysis,” Appl. Soft Comput., vol. 34, pp. 728–735, Sep. 2015, doi: 10.1016/J.ASOC.2015.06.009.
  • [39] K. Song, Y. Yan, Y. Zhao, and C. Liu, “Adjacent evaluation of local binary pattern for texture classification,” J. Vis. Commun. Image Represent., vol. 33, pp. 323–339, Nov. 2015, doi: 10.1016/j.jvcir.2015.09.016.
  • [40] X. Hong, G. Zhao, M. Pietikäinen, and X. Chen, “Combining LBP difference and feature correlation for texture description,” IEEE Trans. Image Process., vol. 23, no. 6, pp. 2557–2568, 2014, doi: 10.1109/TIP.2014.2316640.
  • [41] A. Fathi and A. R. Naghsh-Nilchi, “Noise tolerant local binary pattern operator for efficient texture analysis,” Pattern Recognit. Lett., vol. 33, no. 9, pp. 1093–1100, Jul. 2012, doi: 10.1016/j.patrec.2012.01.017.
  • [42] O. García-Olalla, E. Alegre, L. Fernández-Robles, and V. González-Castro, “Local oriented statistics information booster (LOSIB) for texture classification,” in Proceedings - International Conference on Pattern Recognition, 2014, pp. 1114–1119, doi: 10.1109/ICPR.2014.201.
  • [43] T. Song, L. Xin, C. Gao, G. Zhang, and T. Zhang, “Grayscale-Inversion and Rotation Invariant Texture Description Using Sorted Local Gradient Pattern,” IEEE Signal Process. Lett., vol. 25, no. 5, pp. 625–629, May 2018, doi: 10.1109/LSP.2018.2809607.
  • [44] M. H. Shakoor and R. Boostani, “A novel advanced local binary pattern for image-based coral reef classification,” Multimed. Tools Appl., vol. 77, no. 2, pp. 2561–2591, Jan. 2018, doi: 10.1007/s11042-017-4394-6.
  • [45] S. Du, Y. Yan, and Y. Ma, “Local spiking pattern and its application to rotation- and illumination-invariant texture classification,” Optik (Stuttg)., vol. 127, no. 16, pp. 6583–6589, Aug. 2016, doi: 10.1016/j.ijleo.2016.04.002.
  • [46] S. Wang, Q. Wu, X. He, J. Yang, and Y. Wang, “Local N-Ary Pattern and Its Extension for Texture Classification,” IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 9, pp. 1495–1506, Sep. 2015, doi: 10.1109/TCSVT.2015.2406198.
  • [47] T. Song et al., “Noise-robust texture description using local contrast patterns via global measures,” IEEE Signal Process. Lett., vol. 21, no. 1, pp. 93–96, 2014, doi: 10.1109/LSP.2013.2293335.
  • [48] X. Qi, R. Xiao, C. G. Li, Y. Qiao, J. Guo, and X. Tang, “Pairwise rotation invariant co-occurrence local binary pattern,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 11, pp. 2199–2213, Nov. 2014, doi: 10.1109/TPAMI.2014.2316826.
  • [49] Y. Guo, G. Zhao, and M. Pietikäinen, “Discriminative features for texture description,” Pattern Recognit., vol. 45, no. 10, pp. 3834–3843, Oct. 2012, doi: 10.1016/j.patcog.2012.04.003.
  • [50] Z. Guo, Q. Li, J. You, D. Zhang, and W. Liu, “Local directional derivative pattern for rotation invariant texture classification,” Neural Comput. Appl., vol. 21, no. 8, pp. 1893–1904, Apr. 2012, doi: 10.1007/s00521-011-0586-6.
  • [51] Z. Guo, L. Zhang, and D. Zhang, “Rotation invariant texture classification using LBP variance (LBPV) with global matching,” Pattern Recognit., vol. 43, no. 3, pp. 706–719, Mar. 2010, doi: 10.1016/j.patcog.2009.08.017.
  • [52] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002, doi: 10.1109/TPAMI.2002.1017623.
  • [53] I. El Khadiri, M. Kas, Y. El Merabet, Y. Ruichek, and R. Touahni, “Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification,” Inf. Sci. (Ny)., vol. 467, pp. 634–653, Oct. 2018, doi: 10.1016/J.INS.2018.02.009.
  • [54] A. Ramírez Rivera, J. Rojas Castillo, and O. Chae, “Local Directional Texture Pattern image descriptor,” Pattern Recognit. Lett., vol. 51, pp. 94–100, Jan. 2015, doi: 10.1016/j.patrec.2014.08.012.
  • [55] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. Pietikäinen, “Median Robust Extended Local Binary Pattern for Texture Classification,” IEEE Trans. Image Process., vol. 25, no. 3, pp. 1368–1381, Mar. 2016, doi: 10.1109/TIP.2016.2522378.
  • [56] Q. Kou, D. Cheng, L. Chen, and Y. Zhuang, “Principal curvatures based local binary pattern for rotation invariant texture classification,” Optik (Stuttg)., vol. 193, p. 162999, Sep. 2019, doi: 10.1016/J.IJLEO.2019.162999.
  • [57] S. Shojaeilangari, W. Y. Yau, J. Li, and E. K. Teoh, “Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition,” in 2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 166–170, doi: 10.1109/ICARCV.2012.6485152.
  • [58] S. M. Z. Ishraque, A. K. M. H. Banna, and O. Chae, “Local Gabor directional pattern for facial expression recognition,” in Proceeding of the 15th International Conference on Computer and Information Technology, ICCIT 2012, 2012, pp. 164–167, doi: 10.1109/ICCITechn.2012.6509762.
  • [59] W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for face representation and recognition,” in Proceedings of the IEEE International Conference on Computer Vision, 2005, vol. I, pp. 786–791, doi: 10.1109/ICCV.2005.147.

Nötrozofik Doğruluk Temelli Yeni Bir Doku Sınıflandırma Yöntemi

Year 2020, Volume: 3 Issue: 1, 28 - 39, 30.04.2020
https://doi.org/10.35377/saucis.03.01.709186

Abstract

Doku analizi, bilgisayar görmesi ve görüntü işleme alanlarındaki problemlerin çözümünde başvurulan temel işlemlerden biridir. Bu çalışmada, nötrozofik küme temelli yeni bir yerel ikili örüntü (LBP) yöntemi önerilmiştir. Önerilen yöntem NZ - LBP  olarak isimlendirilmiştir. Önerilen NZ - LBP yönteminde doku görüntüsü nötrozofik kümeye dönüştürülür ve doku görüntüsü doğruluk üyelik kümesi ile ifade edilir. Görüntünün yerel ikili örüntü öznitelikleri orijinal giriş görüntüsü yerine nötrozofik doğruluk küme görüntüsü kullanılarak hesaplanır. Nötrozofik üyelik kümeleri orijinal giriş görüntüsüne göre gürültüye karşı daha dayanıklıdır. Nötrozofik küme gürültü bileşenlerini baskılar ve bu sayede kenar bilgileri daha doğru bir şekilde hesaplanabilir. Böylece orjinal görüntünün yerine nötrozofik doğruluk kümesinin kullanılması daha etkili yerel ikili örüntü özniteliklerinin elde edilmesini sağlamıştır. Önerilen yöntem düşük öznitelik boyutu, uygun hesaplama maliyeti ile yüksek sınıflandırma doğrulukları elde edebilmiştir. Deneysel sonuçlar önerilen yöntemin öznitelik boyutunu artırmadan yerel ikili örüntü yönteminin sınıflandırma doğruluğunu yaklaşık 11% artırdığını göstermektedir. Elde edilen sonuçlar önerilen yöntemin gerçek zamanlı uygulamalar için uygulanılabilir olduğunu ortaya koymaktadır.

References

  • [1] G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto, “Dynamic Textures,” Int. J. Comput. Vis., vol. 51, no. 2, pp. 91–109, 2003, doi: 10.1023/A:1021669406132.
  • [2] M. A. Muqeet and R. S. Holambe, “Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition,” Appl. Comput. Informatics, vol. 15, no. 2, pp. 163–171, Jul. 2019, doi: 10.1016/J.ACI.2017.11.002.
  • [3] M. Varma and A. Zisserman, “A Statistical Approach to Texture Classification from Single Images,” Int. J. Comput. Vis., vol. 62, no. 1/2, pp. 61–81, Apr. 2005, doi: 10.1023/B:VISI.0000046589.39864.ee.
  • [4] P. P. Ohanian and R. C. Dubes, “Performance evaluation for four classes of textural features,” Pattern Recognit., vol. 25, no. 8, pp. 819–833, Aug. 1992, doi: 10.1016/0031-3203(92)90036-I.
  • [5] A. Speis and G. Healey, “Feature extraction for texture discrimination via random field models with random spatial interaction,” IEEE Trans. Image Process., vol. 5, no. 4, pp. 635–645, Apr. 1996, doi: 10.1109/83.491339.
  • [6] W.-K. Lam and C.-K. Li, “Rotated texture classification by improved iterative morphological decomposition,” IEE Proc. - Vision, Image, Signal Process., vol. 144, no. 3, p. 171, 1997, doi: 10.1049/ip-vis:19971198.
  • [7] T. Randen and J. H. Husoy, “Filtering for texture classification: a comparative study,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 4, pp. 291–310, Apr. 1999, doi: 10.1109/34.761261.
  • [8] N. Dasgupta and L. Carin, “Texture analysis with variational hidden Markov trees,” IEEE Trans. Signal Process., vol. 54, no. 6, pp. 2353–2356, Jun. 2006, doi: 10.1109/TSP.2006.872588.
  • [9] A. Maleki, B. Rajaei, and H. R. Pourreza, “Rate-Distortion Analysis of Directional Wavelets,” IEEE Trans. Image Process., vol. 21, no. 2, pp. 588–600, Feb. 2012, doi: 10.1109/TIP.2011.2165551.
  • [10] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Trans. Syst. Man. Cybern., vol. SMC-3, no. 6, pp. 610–621, Nov. 1973, doi: 10.1109/TSMC.1973.4309314.
  • [11] G. R. Cross and A. K. Jain, “Markov Random Field Texture Models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-5, no. 1, pp. 25–39, Jan. 1983, doi: 10.1109/TPAMI.1983.4767341.
  • [12] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002, doi: 10.1109/TPAMI.2002.1017623.
  • [13] P. Subudhi and S. Mukhopadhyay, “An efficient graph reduction framework for interactive texture segmentation,” Signal Process. Image Commun., vol. 74, pp. 42–53, May 2019, doi: 10.1016/J.IMAGE.2019.01.010.
  • [14] C. Li, Y. Huang, X. Yang, and H. Chen, “Marginal distribution covariance model in the multiple wavelet domain for texture representation,” Pattern Recognit., vol. 92, pp. 246–257, Aug. 2019, doi: 10.1016/J.PATCOG.2019.04.003.
  • [15] C. Li, Y. Huang, and L. Zhu, “Color texture image retrieval based on Gaussian copula models of Gabor wavelets,” Pattern Recognit., vol. 64, pp. 118–129, Apr. 2017, doi: 10.1016/J.PATCOG.2016.10.030.
  • [16] Y. Song et al., “Gaussian derivative models and ensemble extreme learning machine for texture image classification,” Neurocomputing, vol. 277, pp. 53–64, Feb. 2018, doi: 10.1016/J.NEUCOM.2017.01.113.
  • [17] K. Hanbay, N. Alpaslan, M. F. Talu, and D. Hanbay, “Principal curvatures based rotation invariant algorithms for efficient texture classification,” Neurocomputing, vol. 199, pp. 77–89, Jul. 2016, doi: 10.1016/j.neucom.2016.03.032.
  • [18] K. Hanbay, N. Alpaslan, M. F. Talu, D. Hanbay, A. Karci, and A. F. Kocamaz, “Continuous rotation invariant features for gradient-based texture classification,” Comput. Vis. Image Underst., vol. 132, pp. 87–101, 2015, doi: 10.1016/j.cviu.2014.10.004.
  • [19] X. Bu, Y. Wu, Z. Gao, and Y. Jia, “Deep convolutional network with locality and sparsity constraints for texture classification,” Pattern Recognit., vol. 91, pp. 34–46, Jul. 2019, doi: 10.1016/J.PATCOG.2019.02.003.
  • [20] S. Basu et al., “Deep neural networks for texture classification—A theoretical analysis,” Neural Networks, vol. 97, pp. 173–182, Jan. 2018, doi: 10.1016/J.NEUNET.2017.10.001.
  • [21] J. Zhang, Y. Xie, Q. Wu, and Y. Xia, “Medical image classification using synergic deep learning,” Med. Image Anal., vol. 54, pp. 10–19, May 2019, doi: 10.1016/J.MEDIA.2019.02.010.
  • [22] M. Talo, U. B. Baloglu, Ö. Yıldırım, and U. Rajendra Acharya, “Application of deep transfer learning for automated brain abnormality classification using MR images,” Cogn. Syst. Res., vol. 54, pp. 176–188, May 2019, doi: 10.1016/J.COGSYS.2018.12.007.
  • [23] G. Srivastava and R. Srivastava, “Salient object detection using background subtraction, Gabor filters, objectness and minimum directional backgroundness,” J. Vis. Commun. Image Represent., vol. 62, pp. 330–339, Jul. 2019, doi: 10.1016/J.JVCIR.2019.06.005.
  • [24] X. Zhao, Y. Lin, and J. Heikkila, “Dynamic Texture Recognition Using Volume Local Binary Count Patterns With an Application to 2D Face Spoofing Detection,” IEEE Trans. Multimed., vol. 20, no. 3, pp. 552–566, Mar. 2018, doi: 10.1109/TMM.2017.2750415.
  • [25] Z. Pan, Z. Li, H. Fan, and X. Wu, “Feature based local binary pattern for rotation invariant texture classification,” Expert Syst. Appl., vol. 88, pp. 238–248, Dec. 2017, doi: 10.1016/J.ESWA.2017.07.007.
  • [26] N. Alpaslan and K. Hanbay, “Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification,” IEEE Access, vol. 8, pp. 54415–54430, 2020, doi: 10.1109/ACCESS.2020.2981720.
  • [27] F. Yuan, X. Xia, and J. Shi, “Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns,” Inf. Sci. (Ny)., vol. 460–461, pp. 202–222, Sep. 2018, doi: 10.1016/J.INS.2018.05.033.
  • [28] S. Naeem, F. Riaz, A. Hassan, and R. Nisar, “Description of Visual Content in Dermoscopy Images Using Joint Histogram of Multiresolution Local Binary Patterns and Local Contrast,” in International Conference on Intelligent Data Engineering and Automated Learning, 2015, pp. 433–440, doi: 10.1007/978-3-319-24834-9_50.
  • [29] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of Interest Regions with Center-Symmetric Local Binary Patterns,” in Computer Vision, Graphics and Image Processing, 2006, pp. 58–69, doi: 10.1007/11949619_6.
  • [30] Y. El merabet and Y. Ruichek, “Local Concave-and-Convex Micro-Structure Patterns for texture classification,” Pattern Recognit., vol. 76, pp. 303–322, Apr. 2018, doi: 10.1016/J.PATCOG.2017.11.005.
  • [31] F. Smarandache, A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability, Third edit. American Research Press, 2003.
  • [32] K. Hanbay, “Nötrozofik Küme Temelli Difüzyon Metodu Kullanılarak Görüntülerdeki Örtüşme Problemini Azaltma,” Eur. J. Sci. Technol., no. 18, pp. 505–514, Apr. 2020, doi: 10.31590/ejosat.695191.
  • [33] J. Mohan, V. Krishnaveni, and Y. Guo, “MRI denoising using nonlocal neutrosophic set approach of Wiener filtering,” Biomed. Signal Process. Control, vol. 8, no. 6, pp. 779–791, Nov. 2013, doi: 10.1016/j.bspc.2013.07.005.
  • [34] T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen, and S. Huovinen, “Outex - New framework for empirical evaluation of texture analysis algorithms,” Proc. - Int. Conf. Pattern Recognit., vol. 16, no. 1, pp. 701–706, 2002, doi: 10.1109/icpr.2002.1044854.
  • [35] Y. Xu, H. Ji, and C. Fermüller, “Viewpoint invariant texture description using fractal analysis,” Int. J. Comput. Vis., vol. 83, no. 1, pp. 85–100, Jun. 2009, doi: 10.1007/s11263-009-0220-6.
  • [36] A. Pillai, R. Soundrapandiyan, S. Satapathy, S. C. Satapathy, K. H. Jung, and R. Krishnan, “Local diagonal extrema number pattern: A new feature descriptor for face recognition,” Futur. Gener. Comput. Syst., vol. 81, pp. 297–306, Apr. 2018, doi: 10.1016/j.future.2017.09.055.
  • [37] S. R. Dubey, S. K. Singh, and R. K. Singh, “Local Bit-Plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 4, pp. 1139–1147, Jul. 2016, doi: 10.1109/JBHI.2015.2437396.
  • [38] Y. Kaya, Ö. F. Ertuğrul, and R. Tekin, “Two novel local binary pattern descriptors for texture analysis,” Appl. Soft Comput., vol. 34, pp. 728–735, Sep. 2015, doi: 10.1016/J.ASOC.2015.06.009.
  • [39] K. Song, Y. Yan, Y. Zhao, and C. Liu, “Adjacent evaluation of local binary pattern for texture classification,” J. Vis. Commun. Image Represent., vol. 33, pp. 323–339, Nov. 2015, doi: 10.1016/j.jvcir.2015.09.016.
  • [40] X. Hong, G. Zhao, M. Pietikäinen, and X. Chen, “Combining LBP difference and feature correlation for texture description,” IEEE Trans. Image Process., vol. 23, no. 6, pp. 2557–2568, 2014, doi: 10.1109/TIP.2014.2316640.
  • [41] A. Fathi and A. R. Naghsh-Nilchi, “Noise tolerant local binary pattern operator for efficient texture analysis,” Pattern Recognit. Lett., vol. 33, no. 9, pp. 1093–1100, Jul. 2012, doi: 10.1016/j.patrec.2012.01.017.
  • [42] O. García-Olalla, E. Alegre, L. Fernández-Robles, and V. González-Castro, “Local oriented statistics information booster (LOSIB) for texture classification,” in Proceedings - International Conference on Pattern Recognition, 2014, pp. 1114–1119, doi: 10.1109/ICPR.2014.201.
  • [43] T. Song, L. Xin, C. Gao, G. Zhang, and T. Zhang, “Grayscale-Inversion and Rotation Invariant Texture Description Using Sorted Local Gradient Pattern,” IEEE Signal Process. Lett., vol. 25, no. 5, pp. 625–629, May 2018, doi: 10.1109/LSP.2018.2809607.
  • [44] M. H. Shakoor and R. Boostani, “A novel advanced local binary pattern for image-based coral reef classification,” Multimed. Tools Appl., vol. 77, no. 2, pp. 2561–2591, Jan. 2018, doi: 10.1007/s11042-017-4394-6.
  • [45] S. Du, Y. Yan, and Y. Ma, “Local spiking pattern and its application to rotation- and illumination-invariant texture classification,” Optik (Stuttg)., vol. 127, no. 16, pp. 6583–6589, Aug. 2016, doi: 10.1016/j.ijleo.2016.04.002.
  • [46] S. Wang, Q. Wu, X. He, J. Yang, and Y. Wang, “Local N-Ary Pattern and Its Extension for Texture Classification,” IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 9, pp. 1495–1506, Sep. 2015, doi: 10.1109/TCSVT.2015.2406198.
  • [47] T. Song et al., “Noise-robust texture description using local contrast patterns via global measures,” IEEE Signal Process. Lett., vol. 21, no. 1, pp. 93–96, 2014, doi: 10.1109/LSP.2013.2293335.
  • [48] X. Qi, R. Xiao, C. G. Li, Y. Qiao, J. Guo, and X. Tang, “Pairwise rotation invariant co-occurrence local binary pattern,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 11, pp. 2199–2213, Nov. 2014, doi: 10.1109/TPAMI.2014.2316826.
  • [49] Y. Guo, G. Zhao, and M. Pietikäinen, “Discriminative features for texture description,” Pattern Recognit., vol. 45, no. 10, pp. 3834–3843, Oct. 2012, doi: 10.1016/j.patcog.2012.04.003.
  • [50] Z. Guo, Q. Li, J. You, D. Zhang, and W. Liu, “Local directional derivative pattern for rotation invariant texture classification,” Neural Comput. Appl., vol. 21, no. 8, pp. 1893–1904, Apr. 2012, doi: 10.1007/s00521-011-0586-6.
  • [51] Z. Guo, L. Zhang, and D. Zhang, “Rotation invariant texture classification using LBP variance (LBPV) with global matching,” Pattern Recognit., vol. 43, no. 3, pp. 706–719, Mar. 2010, doi: 10.1016/j.patcog.2009.08.017.
  • [52] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002, doi: 10.1109/TPAMI.2002.1017623.
  • [53] I. El Khadiri, M. Kas, Y. El Merabet, Y. Ruichek, and R. Touahni, “Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification,” Inf. Sci. (Ny)., vol. 467, pp. 634–653, Oct. 2018, doi: 10.1016/J.INS.2018.02.009.
  • [54] A. Ramírez Rivera, J. Rojas Castillo, and O. Chae, “Local Directional Texture Pattern image descriptor,” Pattern Recognit. Lett., vol. 51, pp. 94–100, Jan. 2015, doi: 10.1016/j.patrec.2014.08.012.
  • [55] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. Pietikäinen, “Median Robust Extended Local Binary Pattern for Texture Classification,” IEEE Trans. Image Process., vol. 25, no. 3, pp. 1368–1381, Mar. 2016, doi: 10.1109/TIP.2016.2522378.
  • [56] Q. Kou, D. Cheng, L. Chen, and Y. Zhuang, “Principal curvatures based local binary pattern for rotation invariant texture classification,” Optik (Stuttg)., vol. 193, p. 162999, Sep. 2019, doi: 10.1016/J.IJLEO.2019.162999.
  • [57] S. Shojaeilangari, W. Y. Yau, J. Li, and E. K. Teoh, “Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition,” in 2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 166–170, doi: 10.1109/ICARCV.2012.6485152.
  • [58] S. M. Z. Ishraque, A. K. M. H. Banna, and O. Chae, “Local Gabor directional pattern for facial expression recognition,” in Proceeding of the 15th International Conference on Computer and Information Technology, ICCIT 2012, 2012, pp. 164–167, doi: 10.1109/ICCITechn.2012.6509762.
  • [59] W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for face representation and recognition,” in Proceedings of the IEEE International Conference on Computer Vision, 2005, vol. I, pp. 786–791, doi: 10.1109/ICCV.2005.147.
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Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

Nuh Alpaslan 0000-0002-6828-755X

Publication Date April 30, 2020
Submission Date March 25, 2020
Acceptance Date April 22, 2020
Published in Issue Year 2020Volume: 3 Issue: 1

Cite

IEEE N. Alpaslan, “A Novel Texture Classification Method Based on Neutrosophic Truth”, SAUCIS, vol. 3, no. 1, pp. 28–39, 2020, doi: 10.35377/saucis.03.01.709186.

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