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Encrypted Image Retrieval System Based on Features Analysis | ||
| Al-Mustansiriyah Journal of Science | ||
| Article 1, Volume 28, Issue 3, 2017, Pages 166-173 | ||
| Author | ||
| Methaq T. Gaata | ||
| Abstract | ||
| Content-based search provides an important tool for users to consume the ever-growing digital media repositories. However, since communication between digital products takes place in a public network, the necessity of security for digital images becomes vital. Hence, the design of secure content-based image retrieval system is becoming an increasingly demanding task as never before. In this paper, the secure CBIR with additional improvement for the image retrieval has been presented. The proposed system consists of six phases briefly described as follows: first, feature extraction phase, which produces the low-level quantitative description of the image (color and texture) that used in calculation of similarity score and image indexing. Second, indexing for search process phase, hash table and bloom filter were employed for classification. Third, feature encryption phase, where content protection is performed using a method developed by us (including Chaotic Logistic Map). Fourth, image encryption phase, the chaos and stream cipher systems were applied as an image encryption system in order to achieve image security. Fifth, the retrieval phase, which provides a group of images replying the query based on the similarity score between images, calculated using the extracted features from each image. Finally, Relevance feedback phase, a technique that attempts to capture the user’s needs through iterative feedback. Although the system proved its efficiency in search performance (with 88% of average precision), security strength, and computational complexity, it does not mean the optimal system is designed, since some weakness points still can be found that are suggested to be improved as a future work. | ||
| Keywords | ||
| Content Based Image Retrieval; Secure Content Based Image Retrieval; Image Mining; Similar Image Retrieval; Bloom Filter | ||
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