Secure Content-Based Image Retrieval Using Combined Features in Cloud

Abstract

Secure Content-Based Image Retrieval (SCBIR) is gaining enormous importance due to its applications involving highly sensitive images comprising of medical and personally identifiable data such as clinical decision-making, biometric-matching, and multimedia search. SCBIR on outsourced images is realized by generating secure searchable index from features like color, shape, and texture in unencrypted images. We focus on enhancing the efficiency of SCBIR by combining two visual descriptors which serve as a modified feature descriptor. To improve the efficacy of search, pre-filter tables are generated using Locality Sensitive Hashing (LSH) and resulting adjacent hash buckets are joined to enhance retrieval precision. Top-k relevant images are securely retrieved using Secure k-Nearest Neighbors (kNN) algorithm. Performance of the scheme is evaluated based on retrieval precision and search efficiency on distinct and similar image categories. Experimental results show that the proposed scheme outperforms the existing state of the art SCBIR schemes.