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of Computer Engineering in Research Trends (IJCERT)

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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Augmenting Image Re-Ranking Using Semantic Signatures

Mubasheera Tazeen, G.Somasekhar, Dr.S.Prem Kumar, ,
Affiliations
(M.Tech), CSE
Assistant Professor, Department of Computer Science and Engineering
Professor & HOD, Department of computer science and engineering, G.Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India.
:NOT ASSIGNED


Abstract
Nowadays, Image re-ranking, as an effective way to improve the results of web-based image search. In this paper, a new technique is proposed for web-scale image re-ranking. The mentioned technique is very useful in giving specific results to users in just one click. In this, different semantic spaces for different query keywords can be found offline independently and automatically. Semantic signatures of the images are acquired by projecting their visual features into their related semantic spaces and these semantic signatures are compacted using Hashing techniques At the online stage, images are reranked by comparing their semantic signatures obtained from the visual semantic space specified by the query keyword.


Citation
Mubasheera Tazeen,G.Somasekhar,Dr.S.Prem Kumar."Augmenting Image Re-Ranking Using Semantic Signatures". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 09,pp.608-611, SEPTEMBER - 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I916.pdf,


Keywords : Image re-ranking, query keyword, query image, keyword expansion, visual query expansion, image search, semantic space, semantic signature, Hashing.

References
1. X. Wang, S. Qiu, K. Liu, X. Tang, “Web Image ReRanking Using Query-Specific Semantic Signatures,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 36, no. 4, pp. 810-823, April 2014. 210 
2. W. Y. Ma and B. S. Manjunath, “A toolbox for navigating large image databases, multimedia system,” 3(7), 1999, 184-198. 
3. X. Zhu and J. D. Lafferty, “Semi-supervised Learning using Gaussian Fields and Harmonic Functions,” Proc. 20th Int’l Conf. Machine Learning, 2003, 912-919. 
4. R. Yan, E. Hauptmann, and R. Jin, “Multimedia Search with Pseudo-Relevance Feedback,” in Proc. Int. Conf. Image and Video Retrieval, 2003. 
5. J. Cui, F. Wen, and X. Tang, “Real Time Google and Live Image Search Re-Ranking,” in Proc. 16th ACM Int. Conf. Multimedia, 2008. 
6. Y. Jing and S. Baluja, “Visual Rank: Applying Page Rank to Large- Scale Image Search,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1877-1890, Nov. 2008.
7. J. Cai, Z. Zha, W. Zhou, and Q. Tian, “AttributeAssisted Reranking for Web Image Retrieval,” in Proc. 20th ACM Int. Conf. Multimedia, 2012. 
8. Zauner, C. "Implementation and Benchmarking of Perceptual Image Hash Functions", Master’s Thesis, University of Applied Science, Upper Austria, 2010. 
9. Darshana C. Chaudhari, Prof. Priti Subramanium, “Survey on Web-scale Image Search and Re-ranking with Semantic Signatures,” IJIRCCE, vol. 3(3), pp. 1864- 1867, March 2015.


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Citations Indices All
Citations 1026
h-index 14
i10-index 20
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DOI:10.22362/ijcert