Content Based Reverse Image Search
Main Article Content
Abstract
People can now get access to the required image with a relevant degree of information thanks to the broad improvement of the WWW. Details, photos, flow charts, logos, maps, and other information However, discovering and obtaining relevant information is always a challenge. There are certain text-based search engines, such as Google, that may be used to find desired photographs from the vast pool of images available on the internet. As a result, here need for the pictures online search engine that can search for related and proper images. Content-based image retrieval query approach is same as Reverse image search that involves giving the CBIR device an example photo and having it find photos based on that image. Reverse image search mostly used to find out either information or photos linked to the query image, as well as accurate images.
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How to Cite
Chutel, P., Bhagat, T., Dongre, S., & Chopade, S. (2022). Content Based Reverse Image Search. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 14(01 SPL), 1-5. https://doi.org/10.18090/samriddhi.v14spli01.1
Section
Research Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
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[3] Divyavenkata, divakaryadav.” Image query-based search engine using content-based image retrieval”. 2012 14th International Conference on Modelling and Simulation.
[4] DivyaRagadha, Deepika Kulshreshtha and Divakar Yadav” Techniques for refreshing Images on Web Document”, 2011 International Conference on Control, robotics and Cybernetics.
[5] A. Lakshmi Subrata Rakshit “New Wavelet features for image indexing and retrieval”. 2010 IEEE 2nd International Advance Computing Conference.
[6] P.Praveen Kumar, Aparna , Dr K Venkata Rao PhD.” Compact Descriptors for Accurate Image Indexing and Retrieval: Fcth And Cedd”. International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 8, October – 2012 ISSN: 2278-0181
[7] Ting Yao, Chong-Wah Ngo, Tao Mei.” Circular Reranking for Visual Search”. IEEE Transaction on image processing, vol. 22, no. 4, April 2013.
[8] Dr. K. Seetharaman, M. Kamarasan “A Smart Color Image Retrieval Method Based on Multi resolution Features”, Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on.
[9] Goswami, D, Bhatia, S.K. “RISE: A Robust Image Search Engine” , 2006 IEEE International Conference on Image Processing
[10] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram, Varun Teja Bathini. (2020). Secure Automation Frameworks for Smart Manufacturing Using Blockchain-Assisted Traceability. International Journal of Research & Technology, 8(2), 47–53. Retrieved from https://ijrt.org/j/article/view/879
[11] Tan Hao, Chen Yu, Qiu Hang,”Implementation Of FII-based image retrieval engine”, Apperceiving Computing and Intelligence Analysis,2009. ICACIA 2009. International Conference on
[12] Konstantinos Zagoris, Savvas A. Chatzichristofis, Nikos Papamarkos and Yiannis S. Boutalis, “ img(Anaktisi): A Web Content Based Image Retrieval System” , 2009 Second International Workshop on Similarity Search and Applications.
[13] Harsh Kumar Sarohi, Farhat Ullah Khan,” Image Retrieval using Perceptual Hashing”, IOSR Journal of Computer Engineering (IOSR- JCE) e-ISSN: 2278- 0661, p- ISSN: 2278-8727Volume 9, Issue 1 (Jan. - Feb. 2013), PP 38-40.
[14] Piyush Kansal and Vinay Krishnamurthy,” Reverse Image Search”. Xinmei Tian, Yijuan Lu, Linjun Yang.” Query Difficulty Prediction for Web Image Search”. IEEE Transaction on multimedia, vol. 14, no. 4, August 2012.