Optimum Features selection by fusion using Genetic Algorithm in CBIR

Abstract

The evaluation of the performance of the Content Based Image Retrieval is undertaken for the consideration in this paper. Here the point of the discussion is the performance of the CBIR system using object oriented image segmentation and the evolutionary computational technique. The visual characteristics of the objects such as color, intensity and texture are extracted by the conventional methods. Object oriented image segmentation along with the evolutionary computational technique is proposed here for Image Retrieval Algorithm. Unsupervised Curve evolution method is used for object oriented segmentation of the Image and genetic Algorithm is used for the Optimum Classification and reduction in the Feature dimensionality. The Algorithm is tested on the images which are characterized by the low depth. The Berkeley database is found to be suitable for this purpose. The experimental result shows that the Genetic Algorithm enhances the performance of this Content Based Image Retrieval and found to be suitable for optimization of features selection and compression technique for Feature space. Index terms-CBIR system, color histogram, edge histogram descriptor. Feature vector, genetic algorithm, Image retrieval, texture feature, query image. Dr. Prashant N Chatur has received his M.E. degree in Electronics Engineering from Govt. College of Engineering Amravati, India and Ph. D degree from Amravati University. His area of research includes ANN, Data Mining, Data Stream Mining and Cloud Computing. Currently he is Head of