Square Distance Symmetric Co-occurrence Matrix Thresholding Method Based on Relative Homogeneity

ZHANG Hong; FAN Jiulun

doi:10.1049/cje.2020.05.015

Volume 29 Issue 4

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2020 > 29(4): 686-692

ZHANG Hong and FAN Jiulun, “Square Distance Symmetric Co-occurrence Matrix Thresholding Method Based on Relative Homogeneity,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 686-692, 2020, doi: 10.1049/cje.2020.05.015

Citation:

ZHANG Hong and FAN Jiulun, “Square Distance Symmetric Co-occurrence Matrix Thresholding Method Based on Relative Homogeneity,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 686-692, 2020, doi: 10.1049/cje.2020.05.015

Citation:

PDF( 1724 KB)

Square Distance Symmetric Co-occurrence Matrix Thresholding Method Based on Relative Homogeneity

doi: 10.1049/cje.2020.05.015

ZHANG Hong^1,2,
FAN Jiulun^1,2

1.
Xi'an University of Posts and Telecommunications, Xi'an 710121, China;
2.
Automatic Sorting Technology Research Center, State Post Bureau of the People's Republic of China, Xi'an 710121, China

Funds: This work is supported by the National Science Foundation of China (No.61571361, No.61671377).

Received Date: 2016-10-24
Rev Recd Date: 2019-09-08
Publish Date: 2020-07-10

Abstract

Abstract

We studied a novel image thresholding method using symmetric co-occurrence matrix probability information, in which, the relative homogeneity characteristics of the segmented object and background is explored, square distance co-occurrence matrix thresholding criterion is reformed, and the relative criterion of square distance symmetric co-occurrence matrix is proposed. The new thresholding method applies the spatial information of image and takes the relative information between classes into account. The experimental results show that, comparing with the existing correlation methods, the proposed method can extract the object more integrity, and reserve the edge more clearly.
- Image thresholding,
- Symmetric Cooccurrence Matrix,
- Square distance,
- Relative homogeneity

FullText(HTML)

References(29)

References

Q. Sang, Z. L. Lin and S. T. Acton. “Learning automata for image segmentation”, Pattern Recognition Letters, Vol.74, pp.46-52, 2016.

M. Sezgin and B. Sankur. “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging, Vol.13, No.1, pp.146-168, 2004.

D. Tohl, S. Jim and J. Li, “Contrast enhancement by multi-level histogram shape segmentation with adaptive detail enhancement for noise suppression”, Signal Processing: Image Communication, Vol.71, No.2, pp.45-55, 2019.

P. Long, H. X. Lu and A. Wang, “A novel unsupervised two-stage technique in color image segmentation”, Chinese Journal of Electronics, Vol.27, No.2, pp.405-412, 2018.

F. Zhao, J. L. Fan, H. Q. Liu, et al., “Noise robust multiobjective evolutionary clustering image segmentation motivated by intuitionistic fuzzy information”, IEEE Transactions on Fuzzy Systems, Vol.27, No.2, pp.387-401, 2019.

Y. Zhu, H. Yang, Z. H. LYU, et al., “A global optimization fuzzy clustering algorithm based on tabu search”, Acta Electronica Sinica, Vol.47, No.2, pp.289-295, 2019.(in Chinese)

Q. Cai, Y. Q. Liu, J. Cao, et al., “A watershed image segmentation algorithm based on self-adaptive marking and interregional affinity propagation clustering”, Acta Electronica Sinica, Vol.45, No.8, pp.1911-1918, 2017.(in Chinese)

N. Nikbakhsh, Y. Baleghi and H. Agahi, “Maximum mutual information and Tsallis entropy for unsupervised segmentation of tree leaves in natural scenes”, Computers and Electronics in Agriculture, Vol.162, pp.440-449, 2019.

M. Merzougui and A. E. Allaoui, “Region growing segmentation optimized by evolutionary approach and maximum entropy”, Procedia Computer Science, Vol.151, pp.1046-1051, 2019.

J. Ji, X. J. LYU, Y. F. Yao, “A SAR image segment method using gray level reduction based on graph in ICA space”, Chinese Journal of Electronics, Vol.26, No.4, pp.883-888, 2017.

F. Zhao, H. Q. Liu, J. L. Fan, et al., “Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation”, Neurocomputing, No.312, pp.296-309, 2018.

S. Saif, Al-jaboriy, N. N. A. Sjarif, et al., “Acute lymphoblastic leukemia segmentation using local pixel information”, Pattern Recognition Letters, Vol.125, pp.85-90, 2019.

B. Chanda, B. B. Chaudhuri and D. D. Majumder, “On image enhancement and threshold selection using the gray-level cooccurrence matrix”, Pattern recognition Letters, Vol.3, No.2, pp.243-251, 1985.

B. Chanda and D. D. Majumder, “A note on the use of the gray-level cooccurrence matrix in threshold selection”, Signal Processing, Vol.15, No.1, pp.149-167, 1988.

M. Hashimoto and M. Saito, “High-speed and robust image matching using spatially distinctive and temporally stable pixels”, 2011 International Symposium on Optomechatronic Technologies (ISOT), IEEE, Hong Kong, China, pp.1-8, 2011.

D. Liang, S. Kaneko, M. Hashimoto, et al., “Co-occurrence probabilitybased pixel pairs background model for robust object detection in dynamic scenes”, Pattern Recognition, Vol.48, pp.1370-1386, 2015.

J. W, Long, X. J. Shen and H. P. Chen, “Adaptive minimum error thresholding algorithm”, Acta Automatica Sinica, Vol.38, No.7, pp.1134-1144, 2012.

C. I. Chang, K. Chen, J. Wang, et al., “A relative entropybased approach to image thresholding”, Pattern Recognition, Vol.27, No.9, pp.1275-1289, 1994.

I. El-Feghi, N. Adem, M. A. Sid-Ahmed, et al., “Improved cooccurrence matrix as a feature space for relative entropybased image thresholding ”, Proceedings of the Computer Graphics, Imaging and Visualization, 10. 1109. No.49, pp.314-320, 2007.

J. L. Fan and J. Ren, “Symmetric co-occurrence matrix thresholding method based on square distance”, Acta Electronica Sinica, Vol.39, No.10, pp.2277-2281, 2011. (in Chinese)

J. L. Fan and H. Zhang, “A unique relative entropy-based symmetric co-occurrence matrix thresholding with statistical spatial information”, Chinese Journal of Electronics, Vol.24, No.3, pp.622-626, 2015.

N. Otsu, “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems, Man and Cybernetics, Vol.9, No.1, pp.62-66, 1979.

S. C. Chen and D. H. Li, “Image binarization focusing on objects”, Neurocomputing, Vol.69, No.16-18, pp.2411-2415, 2006.

J. L. Fan and B. Lei. “A modified valley-emphasis method for automatic thresholding”, Pattern Recognition Letters, Vol.33, No.6, pp.703-708, 2012.

X. Y. Xu, S. Z. Xu, L. H. Jin, et al., “Characteristic analysis of Otsu threshold and its applications”, Pattern Recognition Letters, Vol.32, No.7, pp.956-961, 2011.

Q. Chen, L. Zhao, J. Lu, et al., “Modified two-dimensional Otsu image segmentation algorithm and fast realization”, IET Image Process., Vol.6, No.4, pp.426-433, 2012.

J. H. Xue and D. M. Titterington, “Median-based image thresholding”, Image and Vision Computing, Vol.29, No.1, pp.631-637, 2011.

Y. K. Lai and L. P. Rosin. “Efficient circular thresholding”, IEEE Transactions On Image Processing, Vol.23, No.3, pp.992-1001, 2014.

M. Sezgin and B. Sankur, “Image multithresholding based on sample moment function”, Proceeding of the 2003 IEEE International Conference on Image Processing, Barcelona, Spain, No.9, pp.415-418, 2003.

Relative Articles

Supplements(0)

Cited By

Proportional views