We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Efficient Re-Ranking of Images from the Web using Bag based Method

by S.keerthana, R.c.narayanan, K.krishnamoorthy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 21
Year of Publication: 2012
Authors: S.keerthana, R.c.narayanan, K.krishnamoorthy
10.5120/5837-8071

S.keerthana, R.c.narayanan, K.krishnamoorthy . Efficient Re-Ranking of Images from the Web using Bag based Method. International Journal of Computer Applications. 42, 21 ( March 2012), 31-35. DOI=10.5120/5837-8071

@article{ 10.5120/5837-8071,
author = { S.keerthana, R.c.narayanan, K.krishnamoorthy },
title = { Efficient Re-Ranking of Images from the Web using Bag based Method },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 21 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number21/5837-8071/ },
doi = { 10.5120/5837-8071 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:56.067813+05:30
%A S.keerthana
%A R.c.narayanan
%A K.krishnamoorthy
%T Efficient Re-Ranking of Images from the Web using Bag based Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 21
%P 31-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Given a textual query in traditional text based image retrieval (TBIR),relevant images are to be re ranked using visual features after the initial text based image search. In this paper, we propose a new bag based re ranking framework for large scale TBIR. We compute this problem as Multiple Instance Learning and Generalized Multiple Instance (GMI) learning method. To address the ambiguities on the instance labels in the positive and negative bags we propose a GMI settings. Also the user log performs the operation of individual user interaction with the system which improves the performance of image retrieval.

References
  1. 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) .
  2. J. Cui, F. Wen,, and X. Tang: Real time Google and live image search re-ranking. In ProcACM Multimedia, (2008).
  3. jen-hao Hsiao. , chu-song chen. , and ming-syanchen : A novel language model based approach for image object mining and re-ranking. 2008 IEEE DOI 10. 1109/ICDM (2008).
  4. Y. Rui, T. S. Huang, and S. Mehrotra: Content-based image retrieval withrelevance feedback in mars. In Proceedings of the IEEE InternationalConference on Image Processing, pages 815–818, 1997.
  5. W. H. Hsu, L. S. Kennedy. , and S. -F. Chang:Video search reranking via information bottleneck principle. In Proceeding of the 14th ACMInternational Conference on Multimedia, pages 35–44, (2006).
  6. Z. -H. Zhou and H. -B. Dai:Exploiting image contents in web search. In Proceedings of the 20th International Joint Conference on ArtificalIntelligence, pages 2928–2933, (2007).
  7. Y. Jing and S. Baluja: Textual query of personal photos facilitated by large scale web data. In Proceeding of the 17th International Conference on World Wide Web, pages 307–316,( 2008).
  8. Florian Schroff. , Antonio Criminisi. , and Andrew Zisserman: Harvesting Image Databases from the Web. Proc. IEEE Conf. Computer Vision and Pattern Recognition, (2011).
  9. MattiaBroilo:A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm OptimizationIn Proceedings 10th Workshop on Multimedia Signal Processing-MMSP, Cairns, Australia, ( 2008).
  10. R. Yan. , A. G. Hauptmann. , and R. Jin: Multimedia search withpseudo-relevance feedback. In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 238–247, (2003).
  11. Y. Jing and S. Baluja: Pagerank for product image search. In Proceeding of the 17th International Conference on World Wide Web, pages 307–316, (2008).
Index Terms

Computer Science
Information Sciences

Keywords

Image Retrieval Re-ranking Search Engine