The week's pick
Random Articles
Reseach Article
Mining User Queries for Image Search using Click-through Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 169 - Number 2 |
| Year of Publication: 2017 |
| Authors: Anuja S. Kulkarni |
10.5120/ijca2017914595
|
Anuja S. Kulkarni . Mining User Queries for Image Search using Click-through Approach. International Journal of Computer Applications. 169, 2 ( Jul 2017), 10-14. DOI=10.5120/ijca2017914595
Abstract
The analysis of user serach for a query can be useful in improving search engine performance. Although the text search has received much importance but a little attention has been proposed for image search. In this paper, we propose influencing advantage of click session information, Click-through logs maintain clicked images information. The visual information of clicked images is used to infer user-image search goals. The Click session information can be used as past users’ implicit guidance for clustering the images, more precise user search goals can be obtained. “Classification” based approach is proposed for autoclassification of user image search. Experimental results demonstrate the effectiveness of the proposed method.
References
- J. Cui, F. Wen, and X. Tang. Intentsearch: interactive on-line image search re-ranking. In MM ’08, pages 997–998, 2008.
- Y. Luo, W. Liu, J. Liu, and X. Tang. Mqsearch: image search by multi-class query. In CHI ’08, pages 49–52, 2008.
- G. P. Nguyen and M. Worring. Optimization of interactive visual-similarity-based search. TOMCCAP, 4(1), 2008.
- R. Yan, A. Natsev, and M. Campbell. Multi-query interactive image and video retrieval -: theory and practice. In CIVR ’08, pages 475–484, 2008.
- Z.-J. Zha, L. Yang, T. Mei, M. Wang, and Z. Wang. Visual query suggestion. In MM ’09, pages 15–24, 2009.
- F. Mahmoudi, J. Shanbehzadeh A. Eftekhari-Moghadam and H. Soltanian- Zadeh, “Image retrieval based on shape similarity by edge orientation autocorrelogram,” IEEE Trans. on Pattern Recognition, vol. 36, pp. 1725-1736, 2003.
- U. Lee, Z. Liu and J. Cho, “Automatic identification of user goals in web search,” WWW, pp. 391-400, 2005.
- X. Li, Y-Y. Wang and A. Acero, “Learning query intent from regularized click graphs,” SIGIR, pp. 339-346, 2008
- X. Wang and C-X. Zhai, “Learn from web search logs to organize search results,” SIGIR, pp. 87-94, 2007.
- H-J. Zeng, Q-C. He, Z.Chen, W-Y. Ma and J. Ma, “Learning to cluster Web search results,” SIGIR, pp. 210-217, 2004.
- Z-J. Zha, L-J. Yang, Z-F. Wang, T-S. Chua and X-S. Hua, “Visual query suggestion: towards capturing user intent in internet image search,” ACM Trans. On Multimedia Comput. Commu. Appl. 6, 3, Article, August 2010.
- Xiao gang Wang, Ken Liu and Xiao Tang „Query-Specific Visual Semantic Spaces for Web Image Re-ranking.. In Proceeding of the 14th ACM International Conference on Multimedia,(2011) .
- Y. Rui, T. S. Huang, and S. Mehrotra, “Content-based image retrieval with relevance feedback in MARS,” in Proc. IEEE Int. Conf. Image Process., 1997, pp. 815–818.
- L. Chen, D. Xu, I. W. Tsang, and J. Luo, “Tag-based web photo retrieval improved by batch mode re-tagging,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn., 2010, pp. 3440–3446.
- W. H. Hsu, L. S. Kennedy, and S.-F. Chang, “Video search reranking via information bottleneck principle,” in Proc. 14th ACM Int. Conf. Multimedia, 2006, pp. 35–44.
- Google Image Search. http://images.google.com/.
- Microsoft Bing Image Search. http://images.bing.com/.
Index Terms
Keywords