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A Novel Texture Classification Method Based on Neutrosophic Truth

Year 2020, , 28 - 39, 30.04.2020

Nuh Alpaslan

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

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.

Keywords

Neutrosophic set local binary pattern texture classification

References

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Nötrozofik Doğruluk Temelli Yeni Bir Doku Sınıflandırma Yöntemi

Year 2020, , 28 - 39, 30.04.2020

Nuh Alpaslan

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

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.

Keywords

Nötrozofik Küme yerel ikili örüntü doku sınıflandırma

References

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  • [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.
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  • [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.
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  • [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.
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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 2020

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.

Cited By

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