Bag-of-Encrypted-Words for Cloud-Based Substance-Driven Image Retrieval (BOEW-SCIR)


Authors : Dr. G. Amudha; Aadhithan. P; Mano. S

Volume/Issue : Volume 9 - 2024, Issue 1 - January

Google Scholar : http://tinyurl.com/yma6cb9w

Scribd : http://tinyurl.com/fwpv7dtu

DOI : https://doi.org/10.5281/zenodo.10650100

Abstract : With the proliferation of digital images, there has been considerable research into Contentbased Image Retrieval (CBIR) techniques. Typically, CBIR services demand substantial computational and storage resources, making it advantageous to outsource these services to cloud servers equipped with abundant resources. However, the challenge arises in ensuring privacy, given the inherent lack of complete trust in cloud servers. In here, we introduce a delegated CBIR approach adapted from a book BOEW model. Our method involves encrypting images through hue value replacement, block and intra-block pixel permutation. Subsequently, cloud server calculates regional histograms from these protected image blocks, binds and employs the resulting cluster centers as encrypted viewable words. It is this approach allows us to construct a Bagof-Encrypted-Words (BOEW) model, representing each hue as a feature vector—specifically, a generalized histogram of the encrypted viewable words. To measure the resemblance between images, we utilize the Manhattan space between feature vectors on the cloud server. Experimental output and a security analysis of our proffered scheme illustrate its search precision and security.

With the proliferation of digital images, there has been considerable research into Contentbased Image Retrieval (CBIR) techniques. Typically, CBIR services demand substantial computational and storage resources, making it advantageous to outsource these services to cloud servers equipped with abundant resources. However, the challenge arises in ensuring privacy, given the inherent lack of complete trust in cloud servers. In here, we introduce a delegated CBIR approach adapted from a book BOEW model. Our method involves encrypting images through hue value replacement, block and intra-block pixel permutation. Subsequently, cloud server calculates regional histograms from these protected image blocks, binds and employs the resulting cluster centers as encrypted viewable words. It is this approach allows us to construct a Bagof-Encrypted-Words (BOEW) model, representing each hue as a feature vector—specifically, a generalized histogram of the encrypted viewable words. To measure the resemblance between images, we utilize the Manhattan space between feature vectors on the cloud server. Experimental output and a security analysis of our proffered scheme illustrate its search precision and security.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe