Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI)

This paper proposes a Novel color image segmentation using Graph cut method by minimizing the weighted energy function. This method is applying a pair of optimal constraints namely: color constraint and gradient constraint. In the state-of-the-art methods, the background and foreground details are manually initialized and used for verifying the smoothness of the region. But in this proposed method, they are dynamically calculated from the input image. This feature of the proposed method can be used in color image segmentation where more number of unique segments exists in a single image. The genetic algorithm is applied to the graph obtained from the graph cut method. The crossover and mutation operators are applied on various subgraphs to populate the different segments.

Download Full-text

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

Research Anthology on Advancements in Quantum Technology ◽

10.4018/978-1-7998-8593-1.ch007 ◽

2021 ◽

pp. 164-196

Author(s):

Sunanda Das ◽

Sourav De ◽

Siddhartha Bhattacharyya

Keyword(s):

Genetic Algorithm ◽

Image Segmentation ◽

Execution Time ◽

Color Image ◽

Computational Cost ◽

Cost Effective ◽

Color Image Segmentation ◽

Modified Genetic Algorithm ◽

True Color Image ◽

True Color

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.

Download Full-text

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

Quantum-Inspired Intelligent Systems for Multimedia Data Analysis - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5219-2.ch003 ◽

2018 ◽

pp. 55-94

Author(s):

Sunanda Das ◽

Sourav De ◽

Siddhartha Bhattacharyya

Keyword(s):

Genetic Algorithm ◽

Image Segmentation ◽

Execution Time ◽

Color Image ◽

Computational Cost ◽

Cost Effective ◽

Color Image Segmentation ◽

Modified Genetic Algorithm ◽

True Color Image ◽

True Color

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.

Download Full-text