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True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm
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.
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