Maximum Entropy Multi-threshold Recursive Algorithm for Circular Histograms Based on Dynamic Programming
Authors: 
Jiulun Fan, Xudong Zhao, and Weihao LiAuthors Info & Claims
Jiulun Fan, Xudong Zhao, and Weihao LiAuthors Info & ClaimsSeptember 2022
Pages 816 - 824
Abstract
Maximum entropy thresholding on circular histogram is a color image thresholding method, which makes full use of the color information of the hue H component in the image HSI color space and can effectively separate the target from the background. However, there are problems of large computational effort and long computation time in the multi-threshold case. In this paper, dynamic programming is used to optimize the numerical computation in the segmentation process of the multi-threshold maximum entropy method, and the circular histogram maximum entropy multi-threshold method based on dynamic programming is given. Experimental comparison with the circular histogram maximum entropy recursive algorithm shows that the performance of the two fast algorithms is comparable at double thresholding; however, the dynamic programming-based maximum entropy on circle thresholding method is faster than the recursive-based maximum entropy on circle thresholding method when the number of thresholds is greater than 2.
References
[1]
Pare S, Kumar A, Singh G K, Image segmentation using multilevel thresholding: A research review[J]. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2020, 44(1): 1-29.
[2]
Mousavirad S J, Ebrahimpour-Komleh H. Human mental search-based multilevel thresholding for image segmentation[J]. Applied Soft Computing, 2020, 97: 105427.
[3]
Xu L, Jia H, Lang C, A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution[J]. IEEE Access, 2019, 7: 19502-19538.
[4]
Borjigin S, Sahoo P K. Color image segmentation based on multi-level Tsallis–Havrda–Charvát entropy and 2D histogram using PSO algorithms[J]. Pattern Recognition, 2019, 92: 107-118.
[5]
Li B, Zheng C H, Huang D S. Locally linear discriminant embedding: An efficient method for face recognition[J]. Pattern Recognition, 2008, 41(12): 3813-3821.
[6]
Mi J X, Huang D S, Wang B, The nearest-farthest subspace classification for face recognition[J]. Neurocomputing, 2013, 113: 241-250.
[7]
Rupinder Kaur and Er Garima Malik. 2014. An image segmentation using improved FCM watershed algorithm and DBMF. J. Image Graph. 2, 2 (2014), 106–112.
[8]
Kazuki Otsuki, Yutaro Iwamoto, Yen-Wei Chen, Akira Furukawa, and Shuzo Kanasaki. 2019. Cine-MR Image Segmentation for Assessment of Small Bowel Motility Function Using 3D U-Net. J. Image Graph. 7, 4 (2019), 134–139.
[9]
Jiaqi Wu, Guangxu Li, Huimin Lu, and Tohru Kamiya. 2021. A Supervoxel Classification Based Method for Multi-organ Segmentation from Abdominal CT Images. J. Image Graph. 9, 1 (2021).
[10]
Bhandari A K, Kumar A, Singh G K. Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms[J]. Expert systems with applications, 2015, 42(22): 8707-8730.
[11]
Gonzalez R C, Woods R E. Digital Image Processing (Second Edition). 2002.
[12]
García-Lamont F, Cervantes J, López A, Classification of Mexican paper currency denomination by extracting their discriminative colors[C]. Mexican International Conference on Artificial Intelligence. Springer, Berlin, Heidelberg, 2013: 403-412.
[13]
Tseng D C, Li Y F, Tung C T. Circular histogram thresholding for color image segmentation[C]//Proceedings of 3rd International Conference on Document Analysis and Recognition. IEEE, 1995, 2: 673-676.
[14]
Wu J, Zeng P, Zhou Y, A novel color image segmentation method and its application to white blood cell image analysis[C]//2006 8th international Conference on Signal Processing. IEEE, 2006, 2.
[15]
Dimov D, Laskov L. Cyclic histogram thresholding and multithresholding[C]. Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing. 2009: 1-8.
[16]
Lai Y K, Rosin P L. Efficient circular thresholding[J]. IEEE Transactions on Image Processing, 2014, 23(3): 992-1001.
[17]
Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer vision, graphics, and image processing, 1985, 29(3): 273-285.
[18]
Kang C, Wu C, Fan J. Entropy-based circular histogram thresholding for color image segmentation[J]. Signal, Image and Video Processing, 2021, 15(1): 129-138.
[19]
Kang C, Wu C, Fan J. Lorenz curve-based entropy thresholding on circular histogram[J]. IEEE Access, 2020, 8: 17025-17038.
[20]
Yang G, Fan J, Wang D. Recursive algorithms of maximum entropy thresholding on circular histogram[J]. Mathematical Problems in Engineering, 2021, 2021.
[21]
Luessi M, Eichmann M, Schuster G M, Framework for efficient optimal multilevel image thresholding[J]. Journal of Electronic Imaging, 2009, 18(1): 013004.
[22]
Ito S, Yoshioka M, Omatu S, An image segmentation method using histograms and the human characteristics of HSI color space for a scene image[J]. Artificial life and robotics, 2006,10(1):6-10.
[23]
Pun T. Entropic thresholding, a new approach[J]. Computer Graphics & Image Processing, 1981,16(3):210-239.
[24]
Pun T. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Signal Processing, 1985,2(3):223-237.
[25]
Merzban M H, Elbayoumi M. Efficient solution of Otsu multilevel image thresholding: A comparative study[J]. Expert Systems with Applications, 2019, 116: 299-309.
[26]
Bastanfard A, Aghaahmadi M, Kelishami A A, Advances in Multimedia Information Processing — PCM 2001[J]. Lecture Notes in Computer Science, 2015,5879(7499):201-204.
[27]
Pan J, Zheng X W, Sun L, Image segmentation based on 2D OTSU and simplified swarm optimization[C]. 2016 international conference on machine learning and cybernetics (ICMLC). IEEE, 2016, 2: 1026-1030.
[28]
Fenster A, Chiu B. Evaluation of segmentation algorithms for medical imaging[C]. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2006: 7186-7189.
[29]
Wang Z, Bovik A C, Sheikh H R, Image quality assessment: from error visibility to structural similarity[J]. IEEE transactions on image processing, 2004, 13(4): 600-612.
Index Terms
- Maximum Entropy Multi-threshold Recursive Algorithm for Circular Histograms Based on Dynamic Programming
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September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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