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

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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.

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References

  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 1–15

  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 207–248

    Google Scholar 

  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):1973–1986

    Article  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  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 716–723

  6. Yacoob Y, Davis L (2007) Segmentation using meta-texture saliency. In: International Conference of Computer Vision (ICCV 2007)

  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):15–28

    Article  Google Scholar 

  8. Savelonas Michalis A, Iakovidis Dimitris K, Maroulis Dimitris (2008) Lbp-guided active contours. Pattern Recognit Lett 29(9):1404–1415

    Article  Google Scholar 

  9. Haindl M, Mikeš S (2004) Model-based texture segmentation. In: Image analysis and recognition, Springer, Berlin, pp 306–313

    Chapter  Google Scholar 

  10. Zhou Hailing, Zheng Jianmin, Wei Lei (2013) Texture aware image segmentation using graph cuts and active contours. Pattern Recognit 46(6):1719–1733

    Article  MATH  Google Scholar 

  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):2325–2336

    Article  Google Scholar 

  12. Julesz B (1994) Dialogues on Perception. The MIT Press, Cambridge, MA

    Google Scholar 

  13. Malik J, Belongie S, Shi J, Leung T (1999) Textons, contours and regions: cue integration in image segmentation. Int Conf Comput Vis 2:918–925

    Google Scholar 

  14. Leung T, Malik J (2001) Representing and recognizing the visual appareance of materials using three-dimensional textons. Int J Comput Vis 43(1):29–44

    Article  MATH  Google Scholar 

  15. van der Maaten LJP, Postma E (2007) Texton-based texture classification. In: Proceedings of Belgium-Netherlands Artificial Intelligence Conference

  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 4078–4085

  17. Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Int J Comput Vis 62(1–2):61–81

    Article  Google Scholar 

  18. Zhu S-C, Guo C-E, Wu Y, Wang Y (2005) What are textons ? Int J Comput Vis 62(1–2):121–143

    Article  MATH  Google Scholar 

  19. Alvarez S, Fermandez, Vanrell M (2012) Texton theory revisited: a bag-of-words approach to combine textons. Pattern Recognit 45(12):4312–4325

    Article  Google Scholar 

  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):999–1013

    Article  Google Scholar 

  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 764–777

    Chapter  Google Scholar 

  22. Kingsbury NG (2001) Complex wavelets for shift invariant analysis and filtering of signals. J Appl Comput Harmon Anal 10(3):234–253

    Article  MathSciNet  MATH  Google Scholar 

  23. De Rivaz P (2000) Complex wavelet based image analysis and synthesis. PhD Thesis, University of Cambridge

  24. Selesnick IW, Baraniuk RG, Kingsbury NG (2005) The dual-treeree complex wavelet transform. IEEE Signal Processing Magazine 22(6):123–151

    Article  Google Scholar 

  25. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3):346–359

    Article  Google Scholar 

  26. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  MATH  Google Scholar 

  27. Lu CS, Chung PC, Chen CF (1997) Unsupervised texture segmentation via wavelet transform. Pattern Recognit 30(5):729–742

    Article  Google Scholar 

  28. Kim SC, Kang TJ (2007) Texture classification and segmentation using wavelet packet frame and gaussian mixture model. Pattern Recognit 40(4):1207–1221

    Article  MATH  Google Scholar 

  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):729–743

    Article  Google Scholar 

  30. Kingsbury NG (2006) Rotation-invariant local feature matching with complex wavelets. In European Conference on Signal Processing (EUSIPCO)

  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 227–230

  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 1027–1035

  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 395–410

  34. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  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. Liang K-H, Tjahjadi T (2006) Adaptative scale fixing for multiscale texture segmentation. IEEE Trans Image Process 15:249–256

    Article  Google Scholar 

  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 1805–1807

  38. Garcia MA, Puig D (2007) Supervised texture classification by integration of multiple texture methods and evaluation windows. Image Vis Comput 25(7):1091–1106

    Article  Google Scholar 

  39. Envi software version 4.7

  40. Robert M, Haralick K, Shanmugam, Its’Hak Dinstein (1973) Textural features for image classification. IEEE Systems, Man, and Cybernetics Society, SMC-3:610–621

  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 437–442

  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):685–706

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Shiva ANR Project (French National Agency for Research http://www.shiva-anr.org/). The high-spatial resolution SPOT5 images were accessed due to the ISIS 2010 program of the French Space Agency (CNES) and SPOT Image (http://www.isis-cnes.fr/). The authors would like to thank J. M. Roger, C. Lelong, V. Deblauwe, R. Bose and M. Jones for their helpful comments, which led to improvements in this study.

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Authors and Affiliations

  1. LIRMM UMR 5506 - CC477, 161 rue Ada, 34095, Montpellier Cedex 5, France

    Pol Kennel & Christophe Fiorio

  2. CIRAD UMR AMAP - TA A51 PS2, 34398, Montpellier Cedex 5, France

    Frederic Borne

  3. IFP, Saint Louis Street, Pondicherry, 605 001, India

    Frederic Borne

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  1. Pol Kennel
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  2. Christophe Fiorio
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  3. Frederic Borne
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Correspondence to Pol Kennel.

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Kennel, P., Fiorio, C. & Borne, F. Supervised image segmentation using Q-Shift Dual-Tree Complex Wavelet Transform coefficients with a texton approach. Pattern Anal Applic 20, 227–237 (2017). https://doi.org/10.1007/s10044-015-0491-1

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  • DOI: https://doi.org/10.1007/s10044-015-0491-1

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