OT-Feature Extraction on Scrambled Images with Instantaneous Clustering for CBIR Scheme in Cloud Computing

A novel feature extraction algorithm using Otsu’s Threshold (OT-features) on scrambled images and the Instantaneous Clustering (IC-CBIR) approach is proposed for Content-Based Image Retrieval in cloud computing. Images are stored in the cloud in an encrypted or scrambled form to preserve the privacy content of the images. The proposed method extracts the features from the scrambled images using the Otsu’s threshold. Initially, the Otsu’s threshold is estimated from the scrambled image and based on this threshold the image is divided into two classes in the first iteration. Again, the new threshold values are estimated from two classes. The difference between the new threshold and the previous threshold gives two features. This process is repeated for number of iteration to obtain the complete OT-features of the scrambled image. This paper also proposes an instantaneous clustering approach (IC-CBIR) where the image is moved into a cluster as soon as the image is uploaded by the image owner. Therefore while retrieving the images, the images near to a particular cluster are matched instead of matching with a complete set of image features in the dataset which reduces the search time. The performance of the proposed algorithm is being tested using four different types of the dataset such as Corel 10K, Misc, Oxford flower, and INRIA Holidays dataset. The experimental evaluation reveals that the proposed method outperforms better than the traditional CBIR algorithm on encrypted images in terms of precision, time of search and time of index construction.