Multilevel Image Segmentation Using OptiMUSIG Activation Function with Fixed and Variable Thresholding: A Comparative Study

Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

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Neural Networks

Bhattacharyya¹

2012

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition

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These networks generally operate in two different modes, viz., supervised and unsupervised modes. The supervised mode of operation requires a supervisor to train the network with a training set of data. Networks operating in unsupervised mode apply topology preservation techniques so as to learn inputs. Representative examples of networks following either of these two modes are presented with reference to their topologies, configurations, types of input-output data and functional characteristics. Recent trends in this computing paradigm are also reported with due regards to the application perspectives.

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Multilevel Image Segmentation Using OptiMUSIG Activation Function with Fixed and Variable Thresholding: A Comparative Study

Cited by 3 publications

References 12 publications

Multilevel Image Segmentation Using Modified Genetic Algorithm (MfGA)‐based Fuzzy C‐Means

Multilevel Image Segmentation Using Modified Genetic Algorithm (MfGA)‐based Fuzzy C‐Means

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

Neural Networks

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