Adaptive Region Growing Color Segmentation for Text Using Irregular Pyramid

  • Poh Kok Loo
  • Chew Lim Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3163)

Abstract

This paper presents the result of an adaptive region growing segmentation technique for color document images using an irregular pyramid structure. The emphasis is in the segmentation of textual components for subsequence extraction in document analysis. The segmentation is done in the RGB color space. A simple color distance measurement and a category of color thresholds are derived. The proposed method utilizes a hybrid approach where color feature based clustering followed by detailed region based segmentation is performed. Clustering is done by merging image color points surrounding a color seed selected dynamically. The clustered regions are then put through a detailed segmentation process where an irregular pyramid structure is utilized. Dynamic and repeating selection of the most suitable seed region, fitting changing local condition during the segmentation, is implemented. The growing of regions is done through the use of multiple seeds growing concurrently. The algorithm is evaluated according to 2 factors and compared with an existing method. The result is encouraging and demonstrates the ability and efficiency of our algorithm in achieving the segmentation task.

Keywords

Color Space Textual Component Seed Region Color Segmentation Color Point 
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.

References

  1. 1.
    Lucchese, L., Mitra, S.K.: Color Image Segmentation: A State-of-the-Art Survey (invited paper), Image Processing, Vision, and Pattern Recognition. Proc. of the Indian National Science Academy 67,A(2), 207–221 (2001)Google Scholar
  2. 2.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color Image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)MATHCrossRefGoogle Scholar
  3. 3.
    Wang, H.: Automatic Character Location and Segmentation in Color Scene Images. In: Proc. of the 11th International Conf. on Image Analysis and Processing (2001)Google Scholar
  4. 4.
    Perroud, T., Sobottka, K., Bunke, H.: Text Extraction from Color Documents – Clustering Approaches in Three and Four Dimensions. In: 6th International Conference on Document Analysis and Recognition (September 2001)Google Scholar
  5. 5.
    Lopresti, D., Zhou, J.: Locating and Recognizing Text in WWW Images. Information Retrieval 2, 177–206 (2000)CrossRefGoogle Scholar
  6. 6.
    Lefevre, S., Mercier, L., Tiberghien, V., Vincent, N.: Multiresolution Color Image Segmentation Applied to Background Extraction in Outdoor Images. In: IS&T European Conf. on Color in Graphics, Image and Vis., pp. 363–367 (2002)Google Scholar
  7. 7.
    Liu, J., Yang, Y.-H.: Multiresolution Color Image Segmentation. IEEE Transactions on PAMI 16 (1994)Google Scholar
  8. 8.
    Gong, Y., Prietti, G., Faloutsos, C.: Image Indexing and Retrieval Based on Human Perceptual Color Clustering. Computer Vision and Pattern Recognition (1998)Google Scholar
  9. 9.
    Meer, P.: Stochastic image pyramids. Comp. Vision, Graphics and Image Proc. 45(3), 269–294 (1989)CrossRefGoogle Scholar
  10. 10.
    Loo, P.K., Tan, C.L.: Using Irregular Pyramid for Text segmentation and Binarization of Gray Scale images. In: Proceedings of the 7th International Conference on Document Analysis and Recognition, August 2003, vol. 1, pp. 594–598 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Poh Kok Loo
    • 1
  • Chew Lim Tan
    • 2
  1. 1.School of Design & the EnvironmentSingapore PolytechnicSingapore
  2. 2.School of ComputingNational University of SingaporeSingapore

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