True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm
- 1 January 2021
- book chapter
- other
- Published by IGI Global
Abstract
In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.Keywords
This publication has 26 references indexed in Scilit:
- Multilevel color image segmentation using modified genetic algorithm (MfGA) inspired fuzzy c-means clusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Color image segmentation using feedforward neural networks with FCMInternational Journal of Automation and Computing, 2016
- Color Image Segmentation by NSGA-II Based ParaOptiMUSIG Activation FunctionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Quantum inspired meta-heuristic algorithms for multi-level thresholding for true colour imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- A Genetic Algorithm for Color Image SegmentationLecture Notes in Computer Science, 2013
- Color image segmentation using parallel OptiMUSIG activation functionApplied Soft Computing, 2012
- A Brief Survey of Color Image Preprocessing and Segmentation TechniquesJournal of Pattern Recognition Research, 2011
- True color image segmentation by an optimized multilevel activation functionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Quantitative evaluation of color image segmentation resultsPattern Recognition Letters, 1998
- Pattern Recognition with Fuzzy Objective Function AlgorithmsPublished by Springer Science and Business Media LLC ,1981