Algorithms For Feature Selection In Content Based Image Retrieval:A Review

CBIR applies to techniques for retrieving similar images from image databases, based on automated feature selection methods. Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of a learning algorithm. In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviours in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. A hybrid meta-heuristic swarm intelligence-based search technique, called mixed gravitational search algorithm (MGSA), is employed. Some feature selection parameters are optimized to reach a maximum precision of the CBIR systems. Meanwhile, feature subset selection is done for the same purpose. Keywords— CBIR,Featue selection,Feature selection Algorithms and Feature selection Apporachess