Advertisement

Finding the Texture Features Characterizing the Most Homogeneous Texture Segment in the Image

  • Alexander GoltsevEmail author
  • Vladimir Gritsenko
  • Ernst Kussul
  • Tatiana Baidyk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

Abstract

We propose an algorithm for finding a set of texture features characterizing the most homogeneous texture area of an input image. The found set of features is intended for extraction of this segment. The algorithm processes any input images in the absence of any preliminary information about the images and, accordingly, without any learning. The essence of the algorithm is as follows. The image is covered with a number of test windows. In each of them, a degree of texture homogeneity is measured. The test window with maximal degree of homogeneity is determined and a representative patch of pixels is detected. The texture features extracted from the detected representative patch is considered as those that best characterize the most homogeneous texture segment. So, the proposed algorithm facilitates solution of the texture segmentation task by providing a segmentation technique with helpful additional information about the analyzed image. A computer program simulating the algorithm has been created. The program is tested on natural grayscale images.

Keywords

Texture Texture feature Texture window Texture segmentation Homogeneous texture 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  2. 2.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  3. 3.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  4. 4.
    Gao, C., Zhow, D., Guo, Y.: Automatic iterative algorithm for image segmentation using a modified pulse-coupled neural network. Neurocomputing 119, 332–338 (2013)CrossRefGoogle Scholar
  5. 5.
    Bhosle, V.V., Pawar, V.P.: Texture segmentation: different methods. International Journal of Soft Computing and Engineering (IJSCE) 3, 69–74 (2013)Google Scholar
  6. 6.
    Khan, M.W.: A survey: Image segmentation techniques. International Journal of Future Computer and Communication 3, 89–93 (2014)CrossRefGoogle Scholar
  7. 7.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision (IJCV) 43, 7–27 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Wolf, L., Huang, X., Martin, I., Metaxas, D.: Patch-Based Texture Edges and Segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 481–493. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Caenen, G., Ferrari, V., Zalesny, A., Van Gool, L.: Analyzing the layout of composite textures. In: 2002 International Workshop on Texture Analysis and Synthesis, pp. 15–20 (2002)Google Scholar
  10. 10.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Texture segmentation by multiscale aggregation of filter responses and shape elements. In: 2003 IEEE International Conference on Computer Vision (ICCV), pp. 716–723 (2003)Google Scholar
  11. 11.
    Donoser, M., Bischof, H.: Using covariance matrices for unsupervised texture segmentation. In: 2008 International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)Google Scholar
  12. 12.
    Todorovic, S., Ahuja, N.: Texel-based texture segmentation. In: 2009 IEEE International Conference on Computer Vision (ICCV), pp. 841–848 (2009)Google Scholar
  13. 13.
    Tivive, F.H.C., Bouzerdoum, A.: Texture classification using convolutional neural networks. In: 2006 IEEE Region 10 Conference, pp. 1–4 (2006)Google Scholar
  14. 14.
    Melendez, J., Puig, D., Garcia, M.A.: Multi-level pixel-based texture classification through efficient prototype selection via normalized cut. Pattern Recognition 43, 4113–4123 (2010)zbMATHCrossRefGoogle Scholar
  15. 15.
    Al-Kadi, O.S.: Supervised texture segmentation: a comparative study. In: 2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–5 (2011)Google Scholar
  16. 16.
    Kussul, E.M., Rachkovskij, D.A., Baidyk, T.N.: On image texture recognition by associative-projective neurocomputer. In: Intelligent Engineering Systems through Artificial Neural Networks Conference (ANNIE), pp. 453–458 (1991)Google Scholar
  17. 17.
    Kussul, E.M., Baidyk, T.N., Lukovich, V.V., Rachkovskij, D.A.: Adaptive neural network classifier with multifloat input coding. In: 6-th Intern. Conf. on Neural Networks and their Industrial and Cognitive Applications (Neuro-Nimes 1993), pp. 25–29 (1993)Google Scholar
  18. 18.
    Goltsev, A.: An assembly neural network for texture segmentation. Neural Networks. 9, 643–653 (1996)CrossRefGoogle Scholar
  19. 19.
    Lukovich, V.V., Goltsev, A.D., Rachkovskij, D.A.: Neural network classifiers for micromechanical equipment diagnostics and micromechanical product quality inspection. In: 5-th European Congress on Intelligent Techniques and Soft Computing (EUFIT 1997), vol. 1, pp. 534–536 (1997)Google Scholar
  20. 20.
    Kussul, E.M., Kasatkina, L.M., Rachkovskij, D.A., Wunsch, D.C.: Application of random threshold neural networks for diagnostics of micro machine tool condition. In: IJCNN 1998, vol. 1, pp. 241–244 (1998)Google Scholar
  21. 21.
    Goltsev, A.D.: Neural Networks with the Assembly Organization, Naukova Dumka, Kiev, Ukraine, p.. 200 (2005). (in Russian)Google Scholar
  22. 22.
    Baidyk, T., Kussul, E., Makeyev, O.: Texture recognition with random subspace neural classifier. In: WSEAS International Conference on Systems Science and Engineering, pp. 319–325 (2005)Google Scholar
  23. 23.
    Makeyev, O., Sazonov, E., Baidyk, T., Martin, A.: Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces. Neurocomputing 71, 1413–1421 (2008)CrossRefGoogle Scholar
  24. 24.
    Kussul, E.M., Baidyk, T.N., Wunsch, D.C.: Neural Networks and Micro Mechanics, p. 210. Springer (2010). ISBN 978-3-642-02534-1Google Scholar
  25. 25.
    Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: 2003 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 699–704 (2003)Google Scholar
  26. 26.
    Clausi, D.A., Deng, H.: Design-based texture feature fusion using Gabor filters and co-occurrence probabilities. IEEE Transactions on Image Processing 14, 925–936 (2005)CrossRefGoogle Scholar
  27. 27.
    Wei, H., Bartels, M.: Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis. In: 2006 International Conference on Pattern Recognition (ICPR 2006), vol. 1, pp. 667–670 (2006)Google Scholar
  28. 28.
    Yang, A.Y., Wright, J., Ma, Y., Shakar, S.: Sastry, Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding 110, 212–225 (2008)CrossRefGoogle Scholar
  29. 29.
    Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1–8 (2003)CrossRefGoogle Scholar
  30. 30.
    Mahbubur Rahman, M.: Unsupervised natural image segmentation using mean histogram features. Journal of Multimedia 7, 332–340 (2012)Google Scholar
  31. 31.
    Rachkovskij, D.A., Misuno, I.S., Slipchenko, S.V.: Vector data transformation using random binary matrices. Cybernetics and Systems Analysis 48, 146–156 (2012)zbMATHCrossRefGoogle Scholar
  32. 32.
    Rachkovskij, D.A., Kussul, E.M., Baidyk, T.N.: Building a world model with structure-sensitive sparse binary distributed representations. Biologically Inspired Cognitive Architectures 3, 64–86 (2013)CrossRefGoogle Scholar
  33. 33.
    Gritsenko, V.I., Rachkovskij, D.A., Goltsev, A.D., Lukovych, V.V., Misuno, I.S., Revunova, E.G., Slipchenko, S.V., Sokolov, A.M., Talayev, S.A.: Neural distributed representation for intelligent information technologies and modeling of thinking. Cybernetics and Computer Engineering 173, 7–24 (2013). (in Russian)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Goltsev
    • 1
    Email author
  • Vladimir Gritsenko
    • 1
  • Ernst Kussul
    • 2
  • Tatiana Baidyk
    • 2
  1. 1.International Research and Training Centre of Informational Technologies and Systems of National Academy of Sciences of UkraineKievUkraine
  2. 2.Centro de Ciencias Aplicadas y Desarrollo TecnológicoUniversidad Nacional Autónoma de MéxicoMéxico D. F.México

Personalised recommendations