Advertisement

Unsupervised Approach for Extracting the Textural Region of Interest from Real Image

  • Woo-Beom Lee
  • Jong-Seok Lim
  • Wook-Hyun Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Neural network is an important technique in many image understanding areas. Then the performance of neural network depends on the separative degree among the input vector extracted from an original image. However, most methods are not enough to understand the contents of a image. Accordingly, we propose a efficient method of extracting a spatial feature from a real image, and segmenting the TROI (: Textural Region Of Interest) from the clustered image without a pre-knowledge. Our approach presents the 2-passing k-means algorithm for extracting a spatial feature from image, and uses the unsupervised learning scheme for the block-based image clustering. Also, a segmentation of the clustered TROI is achieved by tuning 2D Gabor filter to the spatial frequency the clustered region. In order to evaluate the performance of the proposed method, the segmenting quality was measured according to the goodness based on the segmented shape of region. Our experimental results showed that the performance of the proposed method is very successful.

Keywords

Spatial Frequency Spatial Feature Real Image Gabor Filter Gabor Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Randen, T., Husoy, J.: Filtering for Texure Classification: A Comparative Study. IEEE Trans. PAMI 21(4), 291–310 (1999)Google Scholar
  2. 2.
    Idrissa, M., Acheroy, M.: Texture Classification using Gabor Filters. Pattern Recognition Letters 23, 1095–1102 (2002)MATHCrossRefGoogle Scholar
  3. 3.
    Tsai, D., et al.: Optimal Gabor Filter Design for Texture Segmentation using Stochastic Optimazation. Image and Vision Computing 19, 299–316 (2001)CrossRefGoogle Scholar
  4. 4.
    Grigorescu, S., et al.: Comparesion of Texture Feature Based on Gabor Filters. IEEE Trans. Image Precessing 11(10), 1160–1167 (2002)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Lee, W.B., Kim, W.H.: Texture Segmentation by Unsupervised Learning and Histogram Analysis using Boundary Tracing. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 25–32. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Woo-Beom Lee
    • 1
  • Jong-Seok Lim
    • 1
  • Wook-Hyun Kim
    • 1
  1. 1.Department of Computer EngineeringYeungnam UniversityGyeongbukRepublic of Korea

Personalised recommendations