Pattern Analysis and Applications

, Volume 20, Issue 1, pp 227–237 | Cite as

Supervised image segmentation using Q-Shift Dual-Tree Complex Wavelet Transform coefficients with a texton approach

Theoretical Advances

Abstract

In this study, we propose a simple and efficient texture-based algorithm for image segmentation. This method constitutes computing textons and bag of words (BOWs) learned by support vector machine (SVM) classifiers. Textons are composed of local magnitude coefficients that arise from the Q-Shift Dual-Tree Complex Wavelet Transform (DT-CWT) combined with color components. In keeping with the needs of our research context, which addresses land cover mapping from remote images, we use a few small texture patches at the training stage, where other supervised methods usually train fully representative textures. We accounted for the scale and rotation invariance issue of the textons, and three different invariance transforms were evaluated on DT-CWT-based features. The largest contribution of this study is the comparison of three classification schemes in the segmentation algorithm. Specifically, we designed a new scheme that was especially competitive and that uses several classifiers, with each classifier adapted to a specific size of analysis window in texton quantification and trained on a reduced data set by random selection. This configuration allows quick SVM convergence and an easy parallelization of the SVM-bank while maintaining a high segmentation accuracy. We compare classification results with textons made using the well-known maximum response filters bank and speed up robust features features as references. We show that DT-CWT textons provide better distinguishing features in the entire set of configurations tested. Benchmarks of our different method configurations were made over two substantial textured mosaic sets, each composed of 100 grey or color mosaics made up of Brodatz or VisTex textures. Lastly, when applied to remote sensing images, our method yields good region segmentation compared to the ENVI commercial software, which demonstrates that the method could be used to generate land cover maps and is suitable for various purposes in image segmentation.

Keywords

Supervised segmentation Texture analysis Scale- and rotation-invariant features Q-Shift Dual-Tree Complex Wavelet Transform (DT-CWT) Textons Support vector machine (SVM) 

References

  1. 1.
    Shotton J, Winn J, Rother C, Criminisi A (2006) Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1–15Google Scholar
  2. 2.
    Tuceyran M, Jain AK (1998) Chapter 2.1 Texture analysis. The Handbook of Pattern Recognition and Computer Vision. 2nd edn. World Scientific Publishing, Singapore, pp 207–248Google Scholar
  3. 3.
    Soltanian-Zadeh H, Rafiee-Rad F, Pourabdollah-Nejad S (2004) Comparison of multiwavelet, wavelet, haralick, and shape features for microcalcification classification in mammograms. Pattern Recognit 37(10):1973–1986CrossRefGoogle Scholar
  4. 4.
    Fauzi MFA, Lewis PH (2006) Automatic texture segmentation for content-based image retrieval application. Pattern Anal Appl 9(4):307–323. doi:10.1007/s10044-006-0042-x MathSciNetCrossRefGoogle Scholar
  5. 5.
    Galun M, Sharon E, Basri R, Brandt A (2003) Texture segmentation by multiscale aggregation of filter responses and shape elements. In: IEEE International Conference on Computer Vision, vol 1. IEEE Computer Society, pp 716–723Google Scholar
  6. 6.
    Yacoob Y, Davis L (2007) Segmentation using meta-texture saliency. In: International Conference of Computer Vision (ICCV 2007)Google Scholar
  7. 7.
    Lo EHS, Pickering MR, Frater MR, Arnold JF (2011) Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform. Image Vis Comput 29(1):15–28CrossRefGoogle Scholar
  8. 8.
    Savelonas Michalis A, Iakovidis Dimitris K, Maroulis Dimitris (2008) Lbp-guided active contours. Pattern Recognit Lett 29(9):1404–1415CrossRefGoogle Scholar
  9. 9.
    Haindl M, Mikeš S (2004) Model-based texture segmentation. In: Image analysis and recognition, Springer, Berlin, pp 306–313CrossRefGoogle Scholar
  10. 10.
    Zhou Hailing, Zheng Jianmin, Wei Lei (2013) Texture aware image segmentation using graph cuts and active contours. Pattern Recognit 46(6):1719–1733CrossRefMATHGoogle Scholar
  11. 11.
    Sang Hak Lee, Hyung Il Koo, Nam Ik Cho (2010) Image segmentation algorithms based on the machine learning of features. Pattern Recognit Lett 31(14):2325–2336CrossRefGoogle Scholar
  12. 12.
    Julesz B (1994) Dialogues on Perception. The MIT Press, Cambridge, MAGoogle Scholar
  13. 13.
    Malik J, Belongie S, Shi J, Leung T (1999) Textons, contours and regions: cue integration in image segmentation. Int Conf Comput Vis 2:918–925Google Scholar
  14. 14.
    Leung T, Malik J (2001) Representing and recognizing the visual appareance of materials using three-dimensional textons. Int J Comput Vis 43(1):29–44CrossRefMATHGoogle Scholar
  15. 15.
    van der Maaten LJP, Postma E (2007) Texton-based texture classification. In: Proceedings of Belgium-Netherlands Artificial Intelligence ConferenceGoogle Scholar
  16. 16.
    Blas MR, Agrawal M, Sundaresan A, and Konolige K (2008) Fast color/texture segmentation for outdoor robots. In: IEEE / RSJ International Conference on Intelligent Robots and Systems, pp 4078–4085Google Scholar
  17. 17.
    Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Int J Comput Vis 62(1–2):61–81CrossRefGoogle Scholar
  18. 18.
    Zhu S-C, Guo C-E, Wu Y, Wang Y (2005) What are textons ? Int J Comput Vis 62(1–2):121–143CrossRefMATHGoogle Scholar
  19. 19.
    Alvarez S, Fermandez, Vanrell M (2012) Texton theory revisited: a bag-of-words approach to combine textons. Pattern Recognit 45(12):4312–4325CrossRefGoogle Scholar
  20. 20.
    Xu Yong, Huang Sibin, Ji Hui, Ferm++ller Cornelia (2012) Scale-space texture description on sift-like textons. Comput Vis Image Underst 116(9):999–1013CrossRefGoogle Scholar
  21. 21.
    Nguyen H-G, Fablet R, Boucher J-M (2010) Spatial statistics of visual keypoints for texture recognition. In: Daniilidis Kostas, Maragos Petros, Paragios Nikos (eds) Computer Vision ECCV 2010, vol 6314., Lecture Notes in Computer ScienceSpringer, Berlin / Heidelberg, pp 764–777CrossRefGoogle Scholar
  22. 22.
    Kingsbury NG (2001) Complex wavelets for shift invariant analysis and filtering of signals. J Appl Comput Harmon Anal 10(3):234–253MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    De Rivaz P (2000) Complex wavelet based image analysis and synthesis. PhD Thesis, University of CambridgeGoogle Scholar
  24. 24.
    Selesnick IW, Baraniuk RG, Kingsbury NG (2005) The dual-treeree complex wavelet transform. IEEE Signal Processing Magazine 22(6):123–151CrossRefGoogle Scholar
  25. 25.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3):346–359CrossRefGoogle Scholar
  26. 26.
    Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693CrossRefMATHGoogle Scholar
  27. 27.
    Lu CS, Chung PC, Chen CF (1997) Unsupervised texture segmentation via wavelet transform. Pattern Recognit 30(5):729–742CrossRefGoogle Scholar
  28. 28.
    Kim SC, Kang TJ (2007) Texture classification and segmentation using wavelet packet frame and gaussian mixture model. Pattern Recognit 40(4):1207–1221CrossRefMATHGoogle Scholar
  29. 29.
    Elik T, Tjahjadi T (2011) Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands. Comput Electr Eng 37(5):729–743CrossRefGoogle Scholar
  30. 30.
    Kingsbury NG (2006) Rotation-invariant local feature matching with complex wavelets. In European Conference on Signal Processing (EUSIPCO)Google Scholar
  31. 31.
    Lo EHS, Pickering M, Frater M, Arnold J (2004) Scale and rotation invariant texture features from the dual-tree complex wavelet transform. In: IEEE International Conference on Image Processing (Singapore), pp 227–230Google Scholar
  32. 32.
    David Arthur, Sergei Vassilvitskii. K-means++ (2007) The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’07, Society for Industrial and Applied Mathematics, Philadelphia, pp 1027–1035Google Scholar
  33. 33.
    Jones DG, Malik J (1992) A computational framework for determining stereo correspondence from a set of linear spatial filters. In: Image and Vision Computing, pp 395–410Google Scholar
  34. 34.
    Vapnik VN (1998) Statistical learning theory. Wiley, New YorkMATHGoogle Scholar
  35. 35.
    Chih-Chung Chang, Chih-Jen Lin (2011) LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27. http://www.csie.ntu.edu.tw/cjlin/libsvm
  36. 36.
    Liang K-H, Tjahjadi T (2006) Adaptative scale fixing for multiscale texture segmentation. IEEE Trans Image Process 15:249–256CrossRefGoogle Scholar
  37. 37.
    Borne F, Guillobez S (1994) A new approach in remote sensing image analysis for natural environment cartography. In: Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation, International Geoscience and Remote Sensing Symposium (IGARSS), pp 1805–1807Google Scholar
  38. 38.
    Garcia MA, Puig D (2007) Supervised texture classification by integration of multiple texture methods and evaluation windows. Image Vis Comput 25(7):1091–1106CrossRefGoogle Scholar
  39. 39.
    Envi software version 4.7Google Scholar
  40. 40.
    Robert M, Haralick K, Shanmugam, Its’Hak Dinstein (1973) Textural features for image classification. IEEE Systems, Man, and Cybernetics Society, SMC-3:610–621Google Scholar
  41. 41.
    Anderson R, Kingsbury NG, Fauqueur J (2005) Multiscale object features from clustered complex wavelet coefficients. In IEEE 13th Workshop on Statistical Signal Processing, pp 437–442Google Scholar
  42. 42.
    Kurtz Camille, Passat Nicolas, GançArski Pierre, Puissant Anne (2012) Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology. Pattern Recognit 45(2):685–706CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Pol Kennel
    • 1
  • Christophe Fiorio
    • 1
  • Frederic Borne
    • 2
    • 3
  1. 1.LIRMM UMR 5506 - CC477Montpellier Cedex 5France
  2. 2.CIRAD UMR AMAP - TA A51 PS2Montpellier Cedex 5France
  3. 3.IFPPondicherryIndia

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