An efficient image retrieval system with structured query based feature selection and filtering initial level relevant images using range query

Abstract

Content Based Image Retrieval is a proficient way of storing, managing, indexing, searching, browsing, mining or retrieving images from a large image repository. Most of the researchers are intensely competing for developing an efficient and precise image retrieval system with less time and space constraint. The proposed method creates two different techniques to reduce the space and time constraints. The first method develops an efficient CBIR system by reducing a number of features to obtain an optimal feature subset using SQL query based feature selection for the normalized feature set. The second method uses SQL range query to filter out initial level relevant images and further the Euclidean distance is applied to refine the filtered subspace inorder to obtain the most relevant images. Gray-level co-occurrence matrix, Region based image descriptors and dominant color descriptor are used to extract the features. Elapsed time, retrieval precision and recall are the evaluation metrics used to analyze the performance with other image retrieval systems. The experiment was performed on Corel dataset and it shows superior performance over the previous systems.