A dynamic wrapper‐based feature selection for improved precision in content‐based image retrieval

Summary

The content‐based image retrieval (CBIR) has been applied in the image processing as well as pattern recognition. A challenging task in the CBIR research has been through the feature extraction for decreasing any semantic gap that is an active research topic. Here, in this work, there is a texture feature that is extracted from an image, making use of a technique of curvelet transform. This curvelet is selected for a sparse representation that is quite critical for the estimation of images, which have been de‐noised with some inversed problems. The wrapping‐based curvelet transform will be even more robust as well as faster in the time of computation than the Ridge Transform. A technique of feature selection will be brought for selecting the optimal features. The correlation‐based feature selection (CFS) method has been adapted for improving the accuracy of the CBIR systems. The non‐deterministic polynomial (NP)–hard problems may be solved using the chemical reaction optimization (CRO) that has been motivated using the technique of chemical reaction. The benefits of proposed method is a necessity for marginal human intrusion for the purpose of retrieving the images needed from its database. The proposed mechanism has been again assessed depending on the Coral Database, and a performance study is ended using precision, f measure, and recall.