AN EFFICIENT APPROACH TO AN IMAGE RETRIEVAL USING PARTICLE SWARM OPTIMIZATION

Particle swarm optimization (PSO) is a way to grasp user’s semantics through optimized iterative learning. In this paper an adaptive retrieval approach based on the concept of particle swarm optimization is introduced. The content can be in the form of objects, colors, textures, shapes as well as relation between them. By understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval. The best compared method reaches its performance at convergence after 10 iterations, while the evolutionary PSO reaches the same result after half of the iterations, then continuing its growth. When a query image is given, only related images are displayed. And hence it reduces work load. This method is robust, reliable and flexible and time efficient for retrieval of images in an efficient way.