Image Segmentation Recommender Using Bio-inspired Algorithms

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 5

Year of Publication : 2024

Authors : Veni Devi Gopal, G. Shreedevi, Angelina Geetha

10.14445/23488549/IJECE-V11I5P125

How to Cite?

Veni Devi Gopal, G. Shreedevi, Angelina Geetha, "Image Segmentation Recommender Using Bio-inspired Algorithms," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 5, pp. 262-275, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I5P125

Abstract:

An important aspect of image processing is image segmentation. It has been applied to a wide range of tasks, including augmented reality, video surveillance, item detection, and medical picture analysis. Even though many different algorithms have been created for picture segmentation, it has never been easy to determine which approach is appropriate for a particular image. Since every image has unique characteristics, it is impossible to find an algorithm that works for every image. Thus, one of the most difficult tasks is determining which method is best for a certain image. In earlier research, we adapted three meta-heuristic clustering algorithms (Shuffled Frog Leap, firefly, and spider monkey algorithm) and demonstrated their superior performance over the widely used k-means technique. It has also been demonstrated that the three algorithms were able to get over the main drawback of the k-means algorithm, which is its automatic determination of the “k” value. The goal of this work is to create a recommendation system that can identify the optimal segmentation algorithm based on the image input within a minimal time span. The outcomes demonstrated that, in terms of SSIM, FSIM, and time required, the suggested method is capable of recommending the best segmentation algorithm. It was found that the suggested algorithms required less computing time and were more than 90% efficient. The Open Surfaces dataset, the Berkeley Segmentation Dataset and Benchmark (BSDS) were used to test the recommender system.

Keywords:

Firefly algorithm, Image segmentation, k-means clustering, Shuffled frog leap algorithm, Spider monkey algorithm.

References:

[1] Muzaffar M. Eusuff, and Kevin E. Lansey, “Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm,” Journal of Water Resources Planning and Management, vol. 129, no. 3, pp. 210-225, 2003.
[CrossRef] [Google Scholar] [Publisher Link]  
[2] Zhao Liping et al., “Application of Shuffled Frog Leaping Algorithm to an Uncapacitated SLLS Problem,” AASRI Procedia, vol. 1, pp. 226-231, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Xiaodan Zhang et al., “Power Control Algorithm in Cognitive Radio System Based on Modified Shuffled Frog Leaping Algorithm,” AEU - International Journal of Electronics and Communications, vol. 66, no. 6, pp. 448-454, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Wei Chen et al., “Applying Population-Based Evolutionary Algorithms and A Neuro-Fuzzy System for Modeling Landslide Susceptibility,” Catena, vol. 172, pp. 212-231, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Juan Li, “The Shortest Path Optimization Based on Mutation Particle Swarm Optimization Algorithm,” Advanced Materials Research, vol. 1049-1050, pp.1690-1693, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Sankalap Arora, and Ranjit Kaur, “An Escalated Convergent Firefly Algorithm,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 2, pp. 308-315, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Fanming Liu, Fangming Li, and Xin Jing, “Navigability Analysis of Local Gravity Map with Projection Pursuit-Based Selection Method by Using Gravitation Field Algorithm,” IEEE Access, vol. 7, pp. 75873-75889, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Ling Teng, and Hang Li, “Modified Discrete Firefly Algorithm Combining Genetic Algorithm for Traveling Salesman Problem,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 16, no. 1, pp. 424-431, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] V. Rajinikanth, and M.S. Couceiro, “RGB Histogram Based Color Image Segmentation Using Firefly Algorithm,” Procedia Computer Science, vol. 46, pp. 1449-1457, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jing Wang et al., “Firefly Algorithm with Dynamic Attractiveness Model and Its Application on Wireless Sensor Networks,” International Journal of Wireless and Mobile Computing, vol. 13, no. 3, pp. 223-231, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] K. Jagatheesan et al., “Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-Area Power Thermal Systems Using Firefly Algorithm,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 2, pp. 503-515, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Iva Bojic et al., “A Self-Optimizing Mobile Network: Auto-Tuning the Network with Firefly-Synchronized Agents,” Information Sciences, vol. 182, no. 1, pp. 77-92, 2012.
[CrossRef] [Google Scholar] [Publisher Link] 
[13] Ivona Brajević, and Predrag Stanimirović, “An Improved Chaotic Firefly Algorithm for Global Numerical Optimization,” International Journal of Computational Intelligence Systems, vol. 12, no. 1, pp. 131-148, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Leandro Fleck Fadel Miguel, Rafael Holdorf Lopez, and Letícia Fleck Fadel Miguel, “Multimodal Size, Shape, and Topology Optimisation of Truss Structures Using the Firefly Algorithm,” Advances in Engineering Software, vol. 56, pp. 23-37, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[15] P. Weber, and P. Pepłowski, “Gaussian Motion Competing with Levy Flights,” Acta Physica Polonica B, vol. 45, no. 11, pp. 2067-2077, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Lyes Tighzert, Cyril Fonlupt, and Boubekeur Mendil, “A Set of New Compact Firefly Algorithms,” Swarm and Evolutionary Computation, vol. 40, pp. 92-115, 2018. [CrossRef] [Google Scholar] [Publisher Link]
[17] Paweł Kopciewicz, and Szymon Łukasik, “Exploiting Flower Constancy in Flower Pollination Algorithm: Improved Biotic Flower Pollination Algorithm and Its Experimental Evaluation,” Neural Computing and Applications, vol. 32, pp. 11999-12010, 2020.
[CrossRef] [Google Scholar] [Publisher Link]  
[18] Adil Baykasoğlu, and Fehmi Burcin Ozsoydan, “An Improved Firefly Algorithm for Solving Dynamic Multidimensional Knapsack Problems,” Expert Systems with Applications, vol. 41, no. 8, pp. 3712-3725, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Eric Bonabeau, Marco Dorigo, and Guy Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, New York, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jagdish Chand Bansal et al., “Spider Monkey Optimization Algorithm for Numerical Optimization,” Memetic Computing, vol. 6, no. 1, pp. 31-47, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Joydip Dhar, and Surbhi Arora, “Designing Fuzzy Rule Base Using Spider Monkey Optimization Algorithm in Cooperative Framework,” Future Computing and Informatics Journal, vol. 2, no. 1, pp. 31-38, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ajay Sharma et al., “Optimal Placement and Sizing of Capacitor Using Limaçon Inspired Spider Monkey Optimization Algorithm,” Memetic Computing, vol. 9, pp. 311-331, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[23] M. Sridevi, “Image Segmentation Based on Multilevel Thresholding Using Firefly Algorithm,” 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, India, pp. 750-753, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Akash Sharma, and Smriti Sehgal, “Image Segmentation Using Firefly Algorithm,” 2016 International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things: Connect your Worlds, Noida, India, pp. 99-102, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Romana Capor Hrosik et al., “Brain Image Segmentation Based on Firefly Algorithm Combined with K-means Clustering,” Studies in Informatics and Control, vol. 28, no. 2, pp. 167-176, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Amel Tehami, and Hadria Fizazi, “Unsupervised Segmentation of Images Based on Shuffled Frog-Leaping Algorithm,” Journal of Information Processing Systems, vol. 13, no. 2, pp. 370-384, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Himanshu Mittal et al., “A Comprehensive Survey of Image Segmentation: Clustering Methods, Performance Parameters, and Benchmark Datasets,” Multimedia Tools and Applications, vol. 81, pp. 35001-35026, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Pouya Demokri Dizji, Saba Joudaki, and Hoshang Kolivand, “A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account,” Wireless Personal Communications, vol. 125, pp. 3425-3441, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Prabhat R. Singh, Mohamed Abd Elaziz, and Shengwu Xiong, “Modified Spider Monkey Optimization Based on Nelder-Mead Method for Global Optimization,” Expert Systems with Applications, vol. 110, pp. 264-289, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Abhay Sharma, Rekha Chaturvedi, and Anuja Bhargava, “A Novel Opposition Based Improved Firefly Algorithm for Multilevel Image Segmentation,” Multimedia Tools and Applications, vol. 81, pp. 15521-15544, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Siddhartha Bhattacharyya et al., “Quantum Spider Monkey Optimization (QSMO) Algorithm for Automatic Gray-Scale Image Clustering,” 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, pp. 1869-1874, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Swaraj Singh Pal et al., “Multi-Level Thresholding Segmentation Approach Based on Spider Monkey Optimization Algorithm,” Proceedings of the Second International Conference on Computer and Communication Technologies, pp. 273-287, 2015.
[CrossRef] [Google Scholar] [Publisher Link]