A Review of Wrapper Feature Selection in Content Based Image Retrieval Systems

In Content Based Image Retrieval systems, feature selection methods have been used for reducing the semantic gap between the visual features and richness of human semantics. The main aim of feature selection is to determine a minimal feature subset from an image, which can be used to represent the original image features. In many real world problems like content based image retrieval systems, feature selection is an important method that helps to remove noisy, irrelevant or misleading features. For example, by removing these features, learning techniques can improve their accuracy. This paper provides a review of the different wrapper feature selection methods used in content based image retrieval systems.