| Title | Modified Feature Descriptors to enhance Secure Content-based Image Retrieval in Cloud |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Anju, J., Shreelekshmi, R. |
| Conference Name | 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) |
| Keywords | color descriptors, composability, content-based image retrieval, content-based retrieval, cryptography, data privacy, encrypted domain gain, feature extraction, image colour analysis, image retrieval, image texture, locality sensitive hashing, Metrics, modified feature descriptors, Outsourced Database Integrity, outsourced encrypted images, outsourced images, pubcrawl, Resiliency, retrieval precision, searchable encrypted index, Searchable encryption, secure CBIR scheme, Secure kNN, texture descriptors, unencrypted images, visual databases, visual descriptors |
| Abstract | With the emergence of cloud, content-based image retrieval (CBIR) on encrypted domain gain enormous importance due to the ever increasing need for ensuring confidentiality, authentication, integrity and privacy of data. CBIR on outsourced encrypted images can be done by extracting features from unencrypted images and generating searchable encrypted index based on it. Visual descriptors like color descriptors, shape and texture descriptors, etc. are employed for similarity search. Since visual descriptors used to represent an image have crucial role in retrieving most similar results, an attempt to combine them has been made in this paper. The effect of combining different visual descriptors on retrieval precision in secure CBIR scheme proposed by Xia et al. is analyzed. Experimental results show that combining visual descriptors can significantly enhance retrieval precision of the secure CBIR scheme. |
| DOI | 10.1109/ICICICT46008.2019.8993195 |
| Citation Key | anju_modified_2019 |
Modified Feature Descriptors to enhance Secure Content-based Image Retrieval in Cloud