Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2014, Vol. 8 Issue (5) : 763-772    https://doi.org/10.1007/s11704-014-3103-0
RESEARCH ARTICLE
A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation
Yunping ZHENG1,*(),Mudar SAREM2
1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
2. School of Software Engineering, Huazhong University of Science and Technology,Wuhan 430074, China
 Download: PDF(427 KB)  
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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.

Keywords binary tree (Bintree)      non-symmetry and anti-packing model (NAM)      coordinate encoding procedure      area calculation      image representation      binary image      linear quadtree (LQT)     
Corresponding Author(s): Yunping ZHENG   
Issue Date: 11 October 2014
 Cite this article:   
Yunping ZHENG,Mudar SAREM. A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation[J]. Front. Comput. Sci., 2014, 8(5): 763-772.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-3103-0
https://academic.hep.com.cn/fcs/EN/Y2014/V8/I5/763
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
https://doi.org/10.1016/j.matcom.2010.04.020
2 Samet H. The quadtree and related hierarchical data structures. Computing Surveys, 1984, 16(2): 187-260
https://doi.org/10.1145/356924.356930
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
https://doi.org/10.1109/TIP.2011.2161322
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
https://doi.org/10.1109/TNNLS.2011.2178472
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
https://doi.org/10.1016/j.jvcir.2011.11.005
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
https://doi.org/10.1109/TPAMI.2011.217
7 Yang B, Li S. Multifocus image fusion and restoration with sparse representation. IEEE Transactions on Instrumentation and Measurement, 2010, 59(4): 884-892
https://doi.org/10.1109/TIM.2009.2026612
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
https://doi.org/10.1016/j.sigpro.2010.07.006
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
https://doi.org/10.1109/TIP.2009.2032890
10 Klinger A. Data structure and pattern recognition. In: Proceedings of 1st International Joint Conference on Pattern Recognition. 1973, 497-498
11 Gargantini I. An effective way to represent quadtrees. Communications of the ACM, 1982, 25(12): 905-910
https://doi.org/10.1145/358728.358741
12 Chen C, Zou H. Linear binary tree. In: Proceedings of 9th International Conference on Pattern Recognition. 1988, 576-578
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
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
https://doi.org/10.1109/TGRS.2011.2160647
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
https://doi.org/10.1109/TGRS.2012.2187340
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
https://doi.org/10.1016/j.compeleceng.2011.07.006
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
https://doi.org/10.4304/jcp.7.12.3028-3035
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
https://doi.org/10.1016/j.jvcir.2012.06.007
19 Kotoulas L, Andreadis I. Accurate calculation of image moments. IEEE Transactions on Image Processing, 2007, 16(8): 2028-2037
https://doi.org/10.1109/TIP.2007.899621
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
https://doi.org/10.1109/83.725368
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
https://doi.org/10.1109/TIP.2007.916157
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
https://doi.org/10.1016/j.patcog.2005.04.004
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
https://doi.org/10.1007/s11704-010-0337-3
24 Li J, Tao D, Li X. A probabilistic model for image representation via multiple patterns. Pattern Recognition, 2012, 45(11): 4044-4053
https://doi.org/10.1016/j.patcog.2012.04.021
25 Zhu H. Image representation using separable two-dimensional continuous and discrete orthogonal moments. Pattern Recognition, 2012, 45(4): 1540-1558
https://doi.org/10.1016/j.patcog.2011.10.002
26 Lin T. Compressed quadtree representations for storing similar images. Image and Vision Computing, 1997, 15(11): 833-843
https://doi.org/10.1016/S0262-8856(97)00031-0
27 Vassilakopoulos M, Manolopoulos Y, Economou K. Overlapping quadtrees for the representation of similar images. Image and Vision Computing, 1993, 11(5): 257-262
https://doi.org/10.1016/0262-8856(93)90002-X
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
https://doi.org/10.1016/S1047-3203(03)00034-8
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
https://doi.org/10.1016/S0262-8856(02)00027-6
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
https://doi.org/10.1016/j.imavis.2008.04.013
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
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
https://doi.org/10.1016/S1007-0214(09)70020-3
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
https://doi.org/10.12733/jics20101795
34 Mohamed S A, Fahmy MM. Binary image compression using efficient partitioning into rectangular regions. IEEE Transactions on Communications, 1995, 43(5): 1888-1892
https://doi.org/10.1109/26.387415
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
https://doi.org/10.1016/j.patcog.2011.07.018
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
https://doi.org/10.1016/j.jvcir.2010.10.002
[1] Yunping ZHENG, Mudar SAREM. A fast algorithm for computing moments of gray images based on NAM and extended shading approach[J]. Front Comput Sci Chin, 2011, 5(1): 57-65.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed