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Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik
Copyright: © 2015 |Pages: 40
ISBN13: 9781466674561|ISBN10: 1466674563|EISBN13: 9781466674578
DOI: 10.4018/978-1-4666-7456-1.ch024
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MLA

Dey, Sandip, et al. "Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding." Research Methods: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2015, pp. 503-542. https://doi.org/10.4018/978-1-4666-7456-1.ch024

APA

Dey, S., Bhattacharyya, S., & Maulik, U. (2015). Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding. In I. Management Association (Ed.), Research Methods: Concepts, Methodologies, Tools, and Applications (pp. 503-542). IGI Global. https://doi.org/10.4018/978-1-4666-7456-1.ch024

Chicago

Dey, Sandip, Siddhartha Bhattacharyya, and Ujjwal Maulik. "Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding." In Research Methods: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 503-542. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7456-1.ch024

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Abstract

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu's algorithm, Renyi's algorithm, Yen's algorithm, Johannsen's algorithm, Silva's algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.

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