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Image segmentation approach based on adaptive flower pollination algorithm and type II fuzzy entropy

  • 1207: Innovations in Multimedia Information Processing & Retrieval​
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Abstract

Image segmentation depend on fuzzy entropy (FE) and intelligent optimization is among the most widely used and popular approaches. Segmentation is an important and pre-processing step in the analysis of an image. Multilevel thresholding is efficient for color images in different multimedia applications in day-to-day life. The method of assessing optimal threshold values using conventional schemes consumes more time. To alleviate the above-mentioned problem, meta-heuristic method has been used for optimization in this area over the last few years. This paper proposes a novel image thresholding technique depend on Adaptive Flower Pollination Algorithm (AFPA) and type II fuzzy entropy (TII-FE). The thresholding methodology is assessed against competitive algorithms concerning the quality, convergence and accuracy of segmented images. The quality is computed in relation of SSIM, PSNR and MSE parameters. The results indicate that AFPA for TII-FE is effective technique for image thresholding.

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References

  1. Abd Elaziz M, Sarkar U, Nag S, Hinojosa S, Oliva D (2020) Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm. Soft Comput 24:14885–14905

    Article  Google Scholar 

  2. Agarwal P, Singh R, Kumar S, Bhattacharya M (2016) Social spider algorithm employed multi-level thresholding segmentation approach. Proc First Int Conf Inf Commun Technol Intell Syst 2:149–259

    Google Scholar 

  3. Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30

    Article  Google Scholar 

  4. Benzid R, Arar D, Bentoumi M (2008) A fast technique for gray level image thresholding and quantization based on the entropy maximization. In: 5th international multi-conference on systems, signals and devices. IEEE, Amman, pp 1–4

    Google Scholar 

  5. Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization-based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41:2538–2560

    Article  Google Scholar 

  6. Chuang LY, Tsai SW, Yang CH (2011) Chaotic catfish particle swarm optimization for solving global numerical optimization problems. Appl Math Comput 217(16):6900–6916

    MathSciNet  MATH  Google Scholar 

  7. Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553

    Article  Google Scholar 

  8. He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing. 240:152–174

    Article  Google Scholar 

  9. Horng M-H, Liou R-J (2011) Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst Appl 38:14805–14811

    Article  Google Scholar 

  10. Jiang YC, Tang Y, Liu H, Chen ZZ (2013) Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets. Inf Sci 240:95–114

    Article  MathSciNet  MATH  Google Scholar 

  11. Kansal I, Kasana SS (2020) Improved color attenuation prior based image de-fogging technique. Multimed Tools Appl 79:12069–12091. https://doi.org/10.1007/s11042-019-08240-6

    Article  Google Scholar 

  12. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for graylevel image thresholding using the entropy of the histogram. Comput Vis Graph Image Proc 29:273–285

    Article  Google Scholar 

  13. Kukreja S, Kasana SS, Kasana G (2019) Histogram based multilevel reversible data hiding scheme using simple and absolute difference images. Multimed Tools Appl 78:6139–6162. https://doi.org/10.1007/s11042-018-6169-0

    Article  MATH  Google Scholar 

  14. Kukreja S, Kasana G, Kasana SS (2020) Curvelet transform based robust copyright protection scheme for color images using extended visual cryptography. Multimed Tools Appl 79:26155–26179. https://doi.org/10.1007/s11042-020-09130-y

    Article  Google Scholar 

  15. Kukreja S, Kasana G, Kasana SS (2021) Extended visual cryptography-based copyright protection scheme for multiple images and owners using LBP–SURF descriptors. The Visual Computer 37(6):1481–1498.

  16. Li CH, Lee CK (1993) Minimum cross entropy thresholding. Pattern Recogn 26:617–625

    Article  Google Scholar 

  17. Martin D, Fowlkes C, Tal D, Malik J (2001) “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics”. In Proceedings eighth IEEE international conference on computer vision, ICCV 2001, vol 2, pp 416–423.

  18. Mlakar U, Potocnik B, Brest J (2016) A hybrid differential evolution for optimal multilevel image thresholding. Expert Syst Appl 65:221–232

    Article  Google Scholar 

  19. Nag S (2017). A type–II fuzzy entropy based multi-level image thresholding using adaptive plant propagation algorithm. https://doi.org/10.31224/osf.io/5kqzd.

  20. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE trans Syst man Cybern 9:62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  21. Pare S, Bhandar AK, Kumar A, Singh GK (2017) An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix. Expert Syst Appl 87:335–362

    Article  Google Scholar 

  22. Singh P, Mittal N (2021) An efficient localization approach to locate sensor nodes in 3D wireless sensor networks using adaptive flower pollination algorithm. Wireless Networks 27(3):1999–2014

  23. Singh U, Salgotra R (2017) Pattern synthesis of linear antenna arrays using enhanced flower pollination algorithm. Int J Antennas Propag. pp. 1-11.

  24. Sumathi R, Venkatesulu M, Arjunan SP (2018) Extracting tumor in MR brain and breast image with Kapur’s entropy based cuckoo search optimization and morphological reconstruction filters. Biocybern Biomed Eng 38(4):918–930

  25. Wu X, Zhou Y, Lu Y (2017) Elite Opposition-Based Water Wave Optimization Algorithm for Global Optimization. Math Prob Eng. 2017 Article ID 3498363, 1, 25

  26. Wu J, Wang YG, Burrage K, Tian YC, Lawson B, Ding Z (2020) An improved firefly algorithm for global continuous optimization problems. Expert Syst Appl 149:113340. https://doi.org/10.1016/j.eswa.2020.113340

    Article  Google Scholar 

  27. Yang XS (2012) Flower pollination algorithm for global optimization. In international conference on unconventional computing and natural computation (pp. 240–249). Springer Berlin Heidelberg, Flower Pollination Algorithm for Global Optimization.

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Authors and Affiliations

  1. Shri Mata Vaishno Devi University, Katra, India

    Shubham Mahajan & Amit Kant Pandit

  2. School of Engineering, Ajeenkya DY Patil University, DY Patil Knowledge City Rd, Charholi Budruk, Lohegaon, Pune, Maharashtra, 412105, India

    Shubham Mahajan

  3. Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, 121102, Palwal, Haryana, India

    Nitin Mittal

Authors
  1. Shubham Mahajan
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  2. Nitin Mittal
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  3. Amit Kant Pandit
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Correspondence to Shubham Mahajan.

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Mahajan, S., Mittal, N. & Pandit, A.K. Image segmentation approach based on adaptive flower pollination algorithm and type II fuzzy entropy. Multimed Tools Appl 82, 8537–8559 (2023). https://doi.org/10.1007/s11042-022-13551-2

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  • DOI: https://doi.org/10.1007/s11042-022-13551-2

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