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A Novel Texture Classification Method Based on Neutrosophic Truth

Yıl 2020, Cilt: 3 Sayı: 1, 28 - 39, 30.04.2020

Nuh Alpaslan

https://doi.org/10.35377/saucis.03.01.709186
Cited By: 1

Öz

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.

Anahtar Kelimeler

Neutrosophic set local binary pattern texture classification

Kaynakça

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Nötrozofik Doğruluk Temelli Yeni Bir Doku Sınıflandırma Yöntemi

Yıl 2020, Cilt: 3 Sayı: 1, 28 - 39, 30.04.2020

Nuh Alpaslan

https://doi.org/10.35377/saucis.03.01.709186
Cited By: 1

Öz

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.

Anahtar Kelimeler

Nötrozofik Küme yerel ikili örüntü doku sınıflandırma

Kaynakça

  • [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.
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Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Makaleler
Yazarlar

Nuh Alpaslan 0000-0002-6828-755X

Yayımlanma Tarihi 30 Nisan 2020
Gönderilme Tarihi 25 Mart 2020
Kabul Tarihi 22 Nisan 2020
Yayımlandığı Sayı Yıl 2020Cilt: 3 Sayı: 1

Kaynak Göster

IEEE N. Alpaslan, “A Novel Texture Classification Method Based on Neutrosophic Truth”, SAUCIS, c. 3, sy. 1, ss. 28–39, 2020, doi: 10.35377/saucis.03.01.709186.

Cited By

Neutrosophic set based local binary pattern for texture classification
Expert Systems with Applications
https://doi.org/10.1016/j.eswa.2022.118350
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    Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science