TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION

Nuo Zhang, Toshinori Watanabe

2012

Abstract

Texture classification is an important technology widely applied in many application fields in image processing. In this study, a novel representation method for texture image is proposed. The proposed approach is based on the consideration of using data compression to search the essential feature of frequent pattern in texture images. Furthermore, to deal with the difficult situation caused by different situations of photography, local binary pattern (LBP) is introduced to the proposed approach to reduce the numbers of varieties of patterns in texture image. Compresibility vector space is adopted in this study instead of learning phase. Based on the patterns extracted by LBP operator which are invariant to monotonic gray-level transformations, data compression helps extract the longest and frequent features. These features provide high analytical ability for texture image. The simulation results will show good performance of our approach.

References

  1. Hu, C. and Liang, H. (2008). Wood surface texture inspection using automatic selection band for wavelet reconstruction. Proceedings of the SPIE - The International Society for Optical Engineering, 7130:713038- 713038-7.
  2. Lazebnik, S., Schmid, C., and Ponce, J. (2005). A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8):1265-1278.
  3. Lewis, M. F. P. (2003). Texture-based image retrieval using multiscale sub-image matching. Proceedings of the SPIE - The International Society for Optical Engineering, 5022:407-416.
  4. Liu, Y., Wu, S., and Zhou, X. (2003). Texture segmentation based on features in wavelet domain for image retrieval. Proceedings of the SPIE - The International Society for Optical Engineering, 5150(1):2026-2034.
  5. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971-987.
  6. Smith, J. and Chang, S.-F. (1996). Automated binary texture feature sets for image retrieval. IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, 4:2239-2242.
  7. Suzuki, M., Yaginuma, Y., and Kodama, H. (2009). A 2d texture image retrieval technique based on texture energy filters. IMAGAPP 2009. First International Conference on Imaging Theory and Applications, pages 145-151.
  8. Ziv, J. and Lempel, A. (1978). Compression of individual sequence via variable-rate coding. IEEE Trans.Inf.Theory, IT-24, (5):530-536.
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Paper Citation


in Harvard Style

Zhang N. and Watanabe T. (2012). TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 437-440. DOI: 10.5220/0003850904370440


in Bibtex Style

@conference{visapp12,
author={Nuo Zhang and Toshinori Watanabe},
title={TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={437-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003850904370440},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION
SN - 978-989-8565-03-7
AU - Zhang N.
AU - Watanabe T.
PY - 2012
SP - 437
EP - 440
DO - 10.5220/0003850904370440