A PSO-ACO Based Hybrid Technique for Partial Shape Fusion applied to Content Based Image Retrieval

  • Dr. Virupakshappa

Abstract

In this paper, proposed novel method of partial feature fusion using Ant Colony Optimization and Particle Swarm Optimization (PSO-ACO) hybrid method for content based image retrieval. The partial feature fusion is combination of two or more partial feature extractor. For the combination of partial feature extractor used geometrical invariant function and some other function based on derivate of transform. The hybrid of PSO-ACO used for the process of feature fusion. The process of feature fusion act in two modes one is local mode and other is global mode. The local mode used ACO algorithm and in global mode used PSO algorithm. The local mode of feature selection set the fitness constraints for the selection of feature in two different feature extractor value of feature fusion. The global mode of features selection iterates the process of most common dominated feature equivalent to input image and precede the process of features fusion. The process of feature fusion incorporates with similarity measure and enhanced the capacity of content based image retrieval. For the validation and performance evaluations of proposed method used MALAB software and coral image dataset.  The values of precision and recall are enhanced instead of individual partial feature based content based image retrieval.

Published
2020-01-31
How to Cite
Dr. Virupakshappa. (2020). A PSO-ACO Based Hybrid Technique for Partial Shape Fusion applied to Content Based Image Retrieval . International Journal of Advanced Science and Technology, 29(1), 1782-1792. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/35863
Section
Articles