Bi-level feature Classification Approach in Privacy Preserving Cloud Network for High-Performance Content-based Image retrieval Mechanism

The enormous storage capacity and easy use of cloud storage have made it extremely popular in recent years. Nevertheless, there's a chance that privacy may be compromised if the picture is uploaded straight to the cloud storage.The content-based image retrieval (CBIR) mechanism proposed in this research maintains the image's privacy content using a Bi-level feature classification approach. This method divides the pixels of the image into two groupssuch aslow and high-level classes. A local encryption technique encrypts the pixels of the low-level class. Two encryption mechanisms are presented in the proposed approachthat uses two different features such as local binary pattern-based position-dependent (LBP-PD) feature and local binary pattern-based position-independent (LBP-PI) feature. The encryption mechanismsare termed asScrambling Without High-Level Component (SWO-HLC) and Scrambling With High-Level Component (SW-HLC). The feature selection factor determines the quantity of high-level features used. The high-level component descriptors from encrypted images were detected by the cloud server and are stored in a storage of the cloud network. The high-level component (HLC) descriptor that are collected from the query encrypted image are compared to the descriptors in the storage of the cloud network to perform the retrieval process. Using the Inria Holidays dataset, the CBIR system was assessed with the criteria including retrieval precision and time complexity. A mean average precision of 62.95% and 67.9% is provided by the SW-HLC and SWO-HLC schemes respectively which is higher than other CBIR schemes used in privacy preserving.