Optimized Color Correlogram based CBIR to beat the hole of Information and Interpretation

This Efficient Systems are the requirement of this digitalized word due to its numerous applications. This Digital World spurts images and raises systems' demand to deal with the image data. The content-based image retrieval system (CBIR) is one of them and also an on-demand system. This system helps the user retrieve similar information or image data based on the provided image and makes life easy to solve many daily life tasks. Government, architecture, academics, engineering, fashion, journalism, and hospitals are examples where the CBIR system efficiently works and reduces manual work requirements. With this heightened trade, the dependency on the automated systems increases, making the systems complex and more vulnerable to complexity issues. CBIR uses multiple features to extract and information and provide consistent and relevant retrieval in revert. Still, the matter of accuracy and complexity is part of it and needs attention from the researchers. This work proposes an optimized color correlogram based CBIR system that utilizes the color and spatial information to contribute efficiency and accuracy in the CBIR system. Ant lion algorithm is here used to optimize low-level and high-level semantics. This proposed system resolves the complexity issue and determines the gap of information and interpretation to provide accurate outcomes. This proposed system's performance was evaluated using the Correl dataset and hence revealed the fitness of the proposed method in terms of precision.