Document Type: Research Article


1 Department of Computer Science, Nesamony Memorial Christian College, Marthandam affiliated to Manonmaniam Sundaranar University, Abishakapatti,Tirunelveli.

2 Nesamony Memorial Christian College, Marthandam affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli.



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.


[1] John Eakins and Margaret Graham. Contentbased image retrieval. 1999.

[2] Ishwar K Sethi, Ioana L Coman, and Daniela Stan. Mining association rules between low-level image features and high-level concepts. In Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, volume 4384, pages 279–290. International Society for Optics and Photonics, 2001.

[3] Shi-Kuo Chang and Shao-Hung Liu. Picture indexing and abstraction techniques for pictorial databases. IEEE Transactions on Pattern Analysis and Machine Intelligence, (4):475–484, 1984. [4] Christos Faloutsos, Ron Barber, Myron Flickner, Jim Hafner, Wayne Niblack, Dragutin Petkovic, and William Equitz. Efficient and effective querying by image content. Journal of intelligent information systems, 3(3-4):231–262, 1994.

[5] Alex Pentland, Rosalind W Picard, and Stan Sclaroff. Photobook: Content-based manipulation of image databases. International journal of computer vision, 18(3):233–254, 1996.

[6] Amarnath Gupta and Ramesh Jain. Visual information retrieval. Communications of the ACM, 40(5):70–79, 1997.

[7] John R Smith and Shih-Fu Chang. Visualseek: a fully automated content-based image query system. In Proceedings of the fourth ACM international conference on Multimedia, pages 87–98, 1997.

[8] Fuhui Long, Hongjiang Zhang, and David Dagan Feng. Fundamentals of content-based image retrieval. In Multimedia information retrieval and management, pages 1–26. Springer, 2003.

[9] Quist-Aphetsi Kester. Image encryption based on the rgb pixel transposition and shuffling. International Journal of Computer Network and Information Security, 5(7):43, 2013.

[10] Musheer Ahmad and M Shamsher Alam. A new algorithm of encryption and decryption of images using chaotic mapping. International Journal on computer science and engineering, 2(1):46–50, 2009.

[11] Peng Yanguo, Cui Jiangtao, Peng Changgen, and Ying Zuobin. Certificateless public key encryption with keyword search. China Communications, 11(11):100–113, 2014.

[12] Dawn Xiaoding Song, David Wagner, and Adrian Perrig. Practical techniques for searches on encrypted data. In Proceeding 2000 IEEE Symposium on Security and Privacy. S&P 2000, pages 44–55. IEEE, 2000.

[13] Dawn Xiaoding Song, David Wagner, and Adrian Perrig. Practical techniques for searches on encrypted data. In Proceeding 2000 IEEE Symposium on Security and Privacy. S&P 2000, pages 44–55. IEEE, 2000.

[14] Reza Curtmola, Juan Garay, Seny Kamara, and Rafail Ostrovsky. Searchable symmetric encryption: improved definitions and efficient constructions. Journal of Computer Security, 19(5):895– 934, 2011.

[15] Zekeriya Erkin, Martin Franz, Jorge Guajardo, Stefan Katzenbeisser, Inald Lagendijk, and Tomas Toft. Privacy-preserving face recognition. In International symposium on privacy enhancing technologies symposium, pages 235–253. Springer, 2009.

[16] Pascal Paillier. Public-key cryptosystems based on composite degree residuosity classes. In International conference on the theory and applications of cryptographic techniques, pages 223–238. Springer, 1999.

[17] Chao-Yung Hsu, Chun-Shien Lu, and Soo-Chang Pei. Secure and robust sift. In Proceedings of the 17th ACM international conference on Multimedia, pages 637–640, 2009. [18] Janez Križaj, Vitomir Štruc, and Nikola Pavešić. Adaptation of sift features for robust face recognition. In International Conference Image Analysis and Recognition, pages 394–404. Springer, 2010.

[19] Unsang Park, Sharath Pankanti, and Anil K Jain. Fingerprint verification using sift features. In Biometric Technology for Human Identification V, volume 6944, page 69440K. International Society for Optics and Photonics, 2008.

[20] Chao-Yung Hsu, Chun-Shien Lu, and Soo-Chang Pei. Image feature extraction in encrypted domain with privacy-preserving sift. IEEE transactions on image processing, 21(11):4593–4607, 2012.

[21] Peijia Zheng and Jiwu Huang. Implementation of the discrete wavelet transform and multiresolution analysis in the encrypted domain. In Proceedings of the 19th ACM international conference on Multimedia, pages 413–422, 2011.

[22] Tiziano Bianchi, Alessandro Piva, and Mauro Barni. On the implementation of the discrete fourier transform in the encrypted domain. IEEE Transactions on Information Forensics and Security, 4(1):86–97, 2009.

[23] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In European conference on computer vision, pages 404–417. Springer, 2006.

[24] Wenjun Lu, Ashwin Swaminathan, Avinash L Varna, and Min Wu. Enabling search over encrypted multimedia databases. In Media Forensics and Security, volume 7254, page 725418. International Society for Optics and Photonics, 2009.

[25] Zhihua Xia, Yi Zhu, Xingming Sun, Zhan Qin, and Kui Ren. Towards privacy-preserving content-based image retrieval in cloud computing. IEEE Transactions on Cloud Computing, 6(1):276–286, 2015.

[26] Hang Cheng, Xinpeng Zhang, Jiang Yu, and Fengyong Li. Markov process-based retrieval for encrypted jpeg images. EURASIP Journal on Information Security, 2016(1):1, 2016.

[27] Degang Xu, Hongtao Xie, and Chenggang Yan. Triple-bit quantization with asymmetric distance for image content security. Machine Vision and Applications, 28(7):771–779, 2017. [28] Reda Bellafqira, Gouenou Coatrieux, Dalel Bouslimi, and Gwénolé Quellec. Content-based image retrieval in homomorphic encryption domain. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2944–2947. IEEE, 2015.

[29] Bernardo Ferreira, Joao Rodrigues, Joao Leitao, and Henrique Domingos. Practical privacypreserving content-based retrieval in cloud image repositories. IEEE Transactions on Cloud Computing, 2017.

[30] Zhihua Xia, Xinhui Wang, Liangao Zhang, Zhan Qin, Xingming Sun, and Kui Ren. A privacypreserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE transactions on information forensics and security, 11(11):2594–2608, 2016. [31] Zhihua Xia, Neal N Xiong, Athanasios V Vasilakos, and Xingming Sun. Epcbir: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Information Sciences, 387:195–204, 2017.

[32] Yanyan Xu, Jiaying Gong, Lizhi Xiong, Zhengquan Xu, Jinwei Wang, and Yun-qing Shi. A privacy-preserving content-based image retrieval method in cloud environment. Journal of Visual Communication and Image Representation, 43:164–172, 2017.

[33] Jiaying Gong, Yanyan Xu, and Xiao Zhao. A privacy-preserving image retrieval method based on improved bovw model in cloud environment. IETE Technical Review, 35(sup1):76–84, 2018.

[34] Meng Shen, Guohua Cheng, Liehuang Zhu, Xiaojiang Du, and Jiankun Hu. Content-based multi-source encrypted image retrieval in clouds with privacy preservation. Future Generation Computer Systems, 109:621–632, 2020.

[35] Chuan Zhang, Liehuang Zhu, Chang Xu, and Rongxing Lu. Ppdp: An efficient and privacypreserving disease prediction scheme in cloudbased e-healthcare system. Future Generation Computer Systems, 79:16–25, 2018.

[36] Nasir Rahim, Jamil Ahmad, Khan Muhammad, Arun Kumar Sangaiah, and Sung Wook Baik. Privacy-preserving image retrieval for mobile devices with deep features on the cloud. Computer Communications, 127:75–85, 2018.

[37] Salahuddin Unar, Xingyuan Wang, Chunpeng Wang, and Yu Wang. A decisive content based image retrieval approach for feature fusion in visual and textual images. Knowledge-Based Systems, 179:8–20, 2019.

[38] Zhihua Xia, Leqi Jiang, Dandan Liu, Lihua Lu, and Byeungwoo Jeon. Boew: A content-based image retrieval scheme using bag-of-encryptedwords in cloud computing. IEEE Transactions on Services Computing, 2019.

[39] Priyanka Singh and Hany Farid. Robust homomorphic image hashing. In CVPR Workshops, pages 11–18, 2019.

[40] Dongmei Li, Xiaolei Dong, Zhenfu Cao, and Haijiang Wang. Privacy-preserving outsourced image feature extraction. Journal of Information Security and Applications, 47:59–64, 2019. [41] Intedhar Shakir Nasir. A new approach for content based image retrieval using statistical metrics. Jour of Adv. Research in Published By: Blue Eyes Intelligence Engineering.

[42] James Ze Wang, Jia Li, and Gio Wiederhold. Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on pattern analysis and machine intelligence, 23(9):947–963, 2001.

[43] Marco La Cascia, Saratendu Sethi, and Stan Sclaroff. Combining textual and visual cues for content-based image retrieval on the world wide web. In Proceedings. IEEE Workshop on Content-Based Access of Image and Video Li-braries (Cat. No. 98EX173), pages 24–28. IEEE, 1998.

[44] Ning Chen. Ci-snf: Exploiting contextual information to improve snf based information retrieval. Information Fusion, 52:175–186, 2019.

[45] Herve Jegou, Matthijs Douze, and Cordelia Schmid. Hamming embedding and weak geometric consistency for large scale image search. In European conference on computer vision, pages 304–317. Springer, 2008.