Relevance Feedback Utilizing Secure Evaluation with Content-based Image Retrieval in Cloud Computing

  • Sonali Sakhahari Panchal Department of Computer Science and Engineering, Matoshri Pratishthan Group of Institutions, Institute of Engineering and Technology, SRTMUN, Nanded
  • Shital Y Gaikwad Department of Computer Science and Engineering Matoshri Pratishthan Group of Institutions, Institute of Engineering and Technology, Swami Ramanand Teerth Marathwada University Nanded (SRTMUN).


Content-based image retrieval (CBIR) is the integrated system of the photograph fetching trouble for instance difficulty of chasing down pictures on a cloud in big datasets. To recognize request semantics and client’s needs if you want to grant submitted consequences with reference to exactness, relevance feedback is combined into CBIR shape. Important evaluation shape will manufacture the precision of yield and will pass at hugest yield. In the watermark-primarily based tradition, a singular watermark is explicitly inserted in blended photos by means of the cloud environment earlier than photos, transmitted towards inquiry mortal. In this way, when an illicit photograph reproduction is located, the illicit inquiry mortal, where appropriates can trail the pictures with the aid of the watermark extraction. Characteristics vectors get assured by using the secure hashing algorithm, analyzing and making ready age are used at image user’s aspect for confirmation motive. TPA (third party auditor) is used to understand enforcement or malevolent activities achieved in cloud circumstances. In our proposed framework, we are including the approach of misrepresentation recognition by generating trapdoor using a hashing calculation, as a document is made with the unique identifier and the client pictures with the names after the link are simplest, a trapdoor is generated.

Keywords: Content-based Image retrieval, Relevance Feedback, trapdoor generation, watermark embedding and extraction, encryption-decryption, and cloud computing


Download data is not yet available.


  1. [1] Zhihua Xia, Xinhui Wang, Liangao Zhang Qin, Xingming Sun, and Kui Ren, “A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing”, in IEEE Transactions on Information Forensics and Security, vol. 11, Issue: 11, pp. 2594-2608, Nov. 2016.

  2. [2] Belattar and S. Mostefai, “CBIR using relevance feedback: comparative analysis and major challenges”, IEEE 2013 5th International Conference on Computer Science and Information Technology, pp. 317-325, 27-28th March 2013.

  3. [3] Aarti Datir, Dipak Patil “Survey on different techniques of CBIR”, in International Journal of Science Technology Management and Research (IJSTMR), vol. 1 Issue 8, pp. 29-34, Nov 2016.

  4. [4] Sakshi Shivhare, Vijay Trivedi and Vineet Richhariya, “Content-based image retrieval by using Interactive relevance feedback technique- A survey”, in International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), vol. 3, Issue: 7, pp. 4641-4646, July 2015.

  5. [5] Urvi H. Panchal, Rohit Srivastava, “A comprehensive survey on digital image watermarking techniques”, in IEEE 2015 Fifth International Conference on Communication Systems and Network Techniques, 591-595, 4-6th April 2015.

  6. [6] Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu and Vincent S. Tseng, “Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns”, in IEEE Transactions On Knowledge And Data Engineering, 23, Issue: 3, pp. 360-372, March 2011.

  7. [7] Zainab Ibrahim Abood, Israa Jameel Muhsin, Nabeel Jameel Tawfiq, “Content-based Image Retrieval (CBIR) Using Hybrid Technique” in International Journal of Computer Applications, Vol. 83, No. 12, pp. 17-24, December 2013.

  8. [8] Ajinkya Sabale, Rohit Prajapati, Sameer Patahn, Sanket Prabhu, Sanjay Agrawal, “Third-party auditing of data on a cloud with fine-grained updates”, in International Journal of Engineering and Computer Science, 4, Issue 11, pp. 14987-14992, Nov 2015.

  9. [9] Jing Xin, Jesse S. Jin, “Relevance feedback for content-based image retrieval using Bayesian Network”, ACM-ICPS ACM International Conference Proceeding Series, VIP ’05 Proceedings of the Pan- Sydney area workshop on Visual information processing, Vol.36, pp. 91-94, 2003.

How to Cite
S. Panchal and S. Gaikwad, “Relevance Feedback Utilizing Secure Evaluation with Content-based Image Retrieval in Cloud Computing”, Adv. J. Grad. Res., vol. 6, no. 1, pp. 31-40, May 2019.
Graduate Research Articles