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A Review on Web-based Image Re-Ranking Techniques

Sujata S. More, Shweta C. Dharmadhikari

Abstract


Image re-ranking is a successful method for the web based image search. In the traditional techniques, the images are search based on only the query keyword given by the user and producing estimated output. But image search based on only query keyword is not efficient. Hence in image re-ranking, user intention is captured by clicking on one query image and retrieved the images related to query image. In this paper we have provided overview of traditional techniques such as Text-only related image search, Attribute based image search and image search using Query image. But these techniques face some limitations hence provide our proposed system which is Image search using semantic signature. Also the semantic signatures are short in size and specific to query keyword, hence this image re-ranking technique improves the accuracy and efficiency of image search.

Keywords


Query Keyword, Image Re-Ranking, Attribute, Query Image, Semantic Signature

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References


R. Datta, D. Joshi, and J.Z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age,” ACM Computing Surveys, vol. 40, article 5, 2007.

J. Krapac, M. Allan, J. Verbeek, and F. Jurie, “Improving Web Image Search Results Using Query-Relative Classifiers,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2010.

C. Lampert, H. Nickisch, and S. Harmeling, “Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2009.

J. Cui, F. Wen, and X. Tang, “Real Time Google and Live Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, 2008.

Xiaogang Wang, Member, IEEE, Shi Qiu, Ke Liu, and Xiaoou Tang, Fellow, IEEE, “Web Image Re-Ranking Using Query-Specific Semantic Signature”, IEEE Transactions, Vol. 36, No-4, April 2014.

J. Cui, F. Wen, and X. Tang, “Intent Search: Interactive on-Line Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, 2008.

X. Tian, L. Yang, J. Wang, X. Wu, and X. Hua, “Bayesian Visual Reranking,” IEEE Trans. Multimedia, vol. 13, no. 4, pp. 639-652, Aug. 2011.

D. Grangier and S. Bengio, “A Discriminative Kernel-Based Model to Rank Images from Text Queries,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1371-1384, Aug. 2008..

J. Lu, J. Zhou, J. Wang, X. Hua, and S. Li, “Image Search Results Refinement via Outlier Detection Using Deep Contexts,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2012.

C. Lampert, H. Nickisch, and S. Harmeling, “Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2009.

N. Rasiwasia, P.J. Moreno, and N. Vasconcelos, “Bridging the Gap: Query by Semantic Example,” IEEE Trans. Multimedia, vol. 9, no. 5, pp. 923-938, Aug. 2007.

A. Farhadi, I. Endres, and D. Hoiem, “Attribute-Centric Recognition for Cross-Category Generalization,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2010.

D. Parikh and K. Grauman, “Relative Attributes,” Proc. Int’l Conf. Computer Vision (ICCV), 2011.

X. Tang, K. Liu, J. Cui, F. Wen, and X. Wang, “Intent Search: Capturing User Intention for One-Click Internet Image Search,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1342-1353, July 2012.

B. Siddiquie, S. Feris, and L. Davis, “Image Ranking and Retrieval Based on Multi-Attribute Queries,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2011.


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