An Optimized Approach for Image Segmentation on Mobile Devices

Hemantkumar R Turkar, Et Al

doi:10.17762/turcomat.v12i2.2341

Abstract

Now a day’s image segmentation is widely used in many multimedia applications. We have introduced the optimized approach for image segmentation based on clustering for use on smart devices. The proposed optimized approach is based on the combination of partitioning of images using quad-tree and Ant Colony Optimization. This approach utilizes the strong ability of ACO i.e global optimization. The proposed optimized algorithm is evaluated on images of standard data set and its performance is compared with existing clustering algorithms. The qualitative and quantitative analysis has been performed to measure the efficacy of the optimized approach over conventional existing algorithms. This procedure obtains better quality results than existing clustering algorithms.

Highlights

An optimization algorithm is a process of executing and comparing various solutions until an optimum solution is found
Optimization approaches are divided into classical optimization techniques and non-classical optimization techniques
The enactment of Fuzzy C-means combined with Particle Swarm optimization method [5] and its variants are studied in various application arenas

Summary

Introduction

An optimization algorithm is a process of executing and comparing various solutions until an optimum solution is found. A classical optimization technique has various methods such as direct method, gradient method, linear programming method and interior point method. Design of various engineering problems is very complex process Formulation of such engineering problems with an objective to satisfy all conditions of design is possible by proper use of various optimization techniques. The classical optimization techniques are beneficial for finding the ideal solution of uninterrupted and differentiable tasks. These techniques are used in analytical methods for differential calculus in locating the ideal solution. The Gradient based optimization techniques are more efficient with considering gradient information. These types of method use the calculus and derivatives of the objectives.

Optimization Techniques

Optimized Approach for Image Segmentation

Conclusion and Future scope