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A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

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Abstract

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure (NAMCEP). By taking some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree (LQT), binary tree (Bintree), non-symmetry and anti-packing model (NAM) with K-lines (NAMK), and NAM representations, we show that NAMCEP can not only reduce the average node, but also simultaneously improve the average compression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algorithm offers significant improvements.

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References

  1. David T, Kempen T V, Huang H, Wilson P. The geometry and dynamics of binary trees. Mathematics and Computers in Simulation, 2011, 81(7): 1464–1481

    Article  MathSciNet  MATH  Google Scholar 

  2. Samet H. The quadtree and related hierarchical data structures. Computing Surveys, 1984, 16(2): 187–260

    Article  MathSciNet  Google Scholar 

  3. Perret B, Lefèvre S, Collet C, Slezak É. Hyperconnections and hierarchical representations for grayscale and multiband image processing. IEEE Transactions on Image Processing, 2012, 21(1): 14–27

    Article  MathSciNet  Google Scholar 

  4. Wei H, Wang X, Lai L L. Compact image representation model based on both nCRF and reverse control mechanisms. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(1): 150–162

    Article  Google Scholar 

  5. Dhara B C, Chanda B. A fast progressive image transmission scheme using block truncation coding by pattern fitting. Journal of Visual Communication and Image Representation, 2012, 23(2): 313–322

    Article  Google Scholar 

  6. Liu H, Wu Z, Li X, Cai D, Huang T S. Constrained nonnegative matrix factorization for image representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1299–1311

    Article  Google Scholar 

  7. Yang B, Li S. Multifocus image fusion and restoration with sparse representation. IEEE Transactions on Instrumentation and Measurement, 2010, 59(4): 884–892

    Article  Google Scholar 

  8. Liu Z, Shen L, Zhang Z. Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction. Signal Processing, 2011, 91(2): 290–299

    Article  MATH  Google Scholar 

  9. Chen Z, Sun S. A Zernike moment phase-based descriptor for local image representation and matching. IEEE Transactions on Image Processing, 2010, 19(1): 205–219

    Article  MathSciNet  Google Scholar 

  10. Klinger A. Data structure and pattern recognition. In: Proceedings of 1st International Joint Conference on Pattern Recognition. 1973, 497–498

    Google Scholar 

  11. Gargantini I. An effective way to represent quadtrees. Communications of the ACM, 1982, 25(12): 905–910

    Article  MATH  Google Scholar 

  12. Chen C, Zou H. Linear binary tree. In: Proceedings of 9th International Conference on Pattern Recognition. 1988, 576–578

    Google Scholar 

  13. Chen T, Su Y, Huang K, Tsai Y, Chien S, Chen L. Visual vocabulary processor based on binary tree architecture for real-time object recognition in full-HD resolution. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2012, 20(12): 2329–2332

    Article  Google Scholar 

  14. Alonso-Gonzalez A, Lopez-Martinez C, Salembier P. Filtering and segmentation of polarimetric SAR data based on binary partition trees. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(2): 593–605

    Article  Google Scholar 

  15. Huang K, Dai D. A new on-board image codec based on binary tree with adaptive scanning order in scan-based mode. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10): 3737–3750

    Article  Google Scholar 

  16. Chen C, Wang G, Sarem M. A new non-symmetry and anti-packing model and its application to image contrast enhancement. Computers and Electrical Engineering, 2011, 37(5): 669–680

    Article  Google Scholar 

  17. Zheng Y, Zhang J, Sarem M. A new image representation method using nonoverlapping non-symmetry and anti-packing model for medical images. Journal of Computers, 2012, 7(12): 3028–3035

    Article  Google Scholar 

  18. Zheng Y, Yu Z, You J, Sarem M. A novel gray image representation using overlapping rectangular NAM and extended shading approach. Journal of Visual Communication and Image Representation, 2012, 23(7): 972–983

    Article  Google Scholar 

  19. Kotoulas L, Andreadis I. Accurate calculation of image moments. IEEE Transactions on Image Processing, 2007, 16(8): 2028–2037

    Article  MathSciNet  Google Scholar 

  20. Spiliotis IM, Mertzios B G. Real time computation of two-dimensional moments on binary images using image block representation. IEEE Transactions on Image Processing, 1998, 7(11): 1609–1615

    Article  Google Scholar 

  21. Lin H, Si J, Abousleman G P. Orthogonal rotation-invariant moments for digital image processing. IEEE Transactions on Image Processing, 2008, 17(3): 272–282

    Article  MathSciNet  Google Scholar 

  22. Chung K, Chen P. An efficient algorithm for computing moments on a block representation of a grey-scale image. Pattern Recognition, 2005, 38(12): 2578–2586

    Article  Google Scholar 

  23. Zheng Y, Sarem M. A fast algorithm for computing moments of gray images based on NAM and extended shading approach. Frontiers of Computer Science in China, 2011, 5(1): 57–65

    Article  MathSciNet  MATH  Google Scholar 

  24. Li J, Tao D, Li X. A probabilistic model for image representation via multiple patterns. Pattern Recognition, 2012, 45(11): 4044–4053

    Article  MATH  Google Scholar 

  25. Zhu H. Image representation using separable two-dimensional continuous and discrete orthogonal moments. Pattern Recognition, 2012, 45(4): 1540–1558

    Article  MATH  Google Scholar 

  26. Lin T. Compressed quadtree representations for storing similar images. Image and Vision Computing, 1997, 15(11): 833–843

    Article  Google Scholar 

  27. Vassilakopoulos M, Manolopoulos Y, Economou K. Overlapping quadtrees for the representation of similar images. Image and Vision Computing, 1993, 11(5): 257–262

    Article  Google Scholar 

  28. Qawasmeh E E. A quadtree-based representation technique for indexing and retrieval of image databases. Journal of Visual Communication and Image Representation, 2003, 14(3): 340–357

    Article  Google Scholar 

  29. Manouvrier M, Rukoz M, Jomier G. Quadtree representations for storage and manipulation of clusters of images. Image and Vision Computing, 2002, 20(7): 513–527

    Article  Google Scholar 

  30. Lin L, Zhu L, Yang F, Jiang T. A novel pixon-representation for image segmentation based on Markov random field. Image and Vision Computing, 2008, 26(11): 1507–1514

    Article  Google Scholar 

  31. Zheng Y, Chen C, Sarem M. A novel algorithm using non-symmetry and anti-packing model with K-lines for binary image representation. In: Proceedings of 1st International Congress on Image and Signal Processing. 2008, 3: 461–465

    Article  Google Scholar 

  32. Zheng Y, Chen C, Mudar S. A NAM representation method for data compression of binary images. Tsinghua Science and Technology, 2009, 14(1): 139–145

    Article  MathSciNet  Google Scholar 

  33. Zheng Y, Zhou W, Mo X. A new NAM-based algorithm for computing Hu moments of binary images. Journal of Information and Computational Science, 2013, 10(8): 2481–2488

    Article  Google Scholar 

  34. Mohamed S A, Fahmy MM. Binary image compression using efficient partitioning into rectangular regions. IEEE Transactions on Communications, 1995, 43(5): 1888–1892

    Article  Google Scholar 

  35. Matsukawa T, Kurita T. Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images. Pattern Recognition, 2012, 45(2): 707–719

    Article  Google Scholar 

  36. Gouiffès M, Zavidovique B. Body color sets: a compact and reliable representation of images. Journal of Visual Communication and Image Representation, 2011, 22(1): 48–60

    Article  Google Scholar 

Download references

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Correspondence to Yunping Zheng.

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Yunping Zheng received his BS degree in computer science from Air Force Radar Academy, Wuhan, China in 2001, and his MS and PhD degrees in computer science from Huazhong University of Science and Technology, Wuhan, China in 2005 and 2008, respectively. He is currently an associate professor with the School of Computer Science and Engineering, South China University of Technology, China. He has published over 70 papers in referred conferences and journals. Dr. Zheng is a member of ACM. His interests include computer graphics, fractal image compression, image processing, and pattern recognition.

Mudar Sarem received his BS degree in electronic engineering from Tishreen University, Lattakia, Syria in 1989, and his MS and PhD degrees in computer science from Huazhong University of Science and Technology, Wuhan, China in 1997 and 2002, respectively. He is currently an associate professor with the School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China. He has published over 50 papers in referred conferences and journals. Dr. Sarem’s research interests include computer graphics, multimedia databases, and image processing.

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Zheng, Y., Sarem, M. A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation. Front. Comput. Sci. 8, 763–772 (2014). https://doi.org/10.1007/s11704-014-3103-0

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