Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 579401, 7 pages
Research Article

The Application of Similar Image Retrieval in Electronic Commerce

1School of Information, Guangdong University of Finance & Economics, Guangzhou 510320, China
2College of Information Engineering, Shanghai Maritime University, Shanghai 20130, China

Received 6 August 2013; Accepted 11 February 2014; Published 22 April 2014

Academic Editors: J. Shu and G. Yue

Copyright © 2014 YuPing Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J.-E. Lee, R. Jin, A. K. Jain, and W. Tong, “Image retrieval in forensics: tattoo image database application,” IEEE Multimedia, vol. 19, no. 1, pp. 40–49, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. X.-F. Wamg, “The analyse of development of network shopping in china,” Economic Research Guide, vol. 40, pp. 174–175, 2009. View at Google Scholar
  3. Iresearch Group, 2012,
  4. M. M. Rahman, S. K. Antani, and G. R. Thoma, “A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 4, pp. 640–646, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. X.-Y. Wang, J.-W. Chen, and H.-Y. Yang, “A new integrated SVM classifiers for relevance feedback content-based image retrieval using em parameter estimation,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2787–2804, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: ideas, influences, and trends of the new age,” ACM Computing Surveys, vol. 40, no. 2, article 5, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Li, Research on content-based image retrieval [Ph.D. thesis], JI Lin University, June 2009.
  8. W. Niblack, R. Barber, W. Equitz et al., “QBIC project: querying images by content, using color, texture, and shape,” in Storage and Retrieval for Image and Video Databases, vol. 1908 of Proceedings of SPIE, pp. 173–187, San Jose, Calif, USA, February 1993. View at Scopus
  9. J. R. Smith and S.-F. Chang, “VisualSEEk: a fully automated content-based image query system,” in Proceedings of the 4th ACM International Multimedia Conference, pp. 87–98, Boston, Mass, USA, November 1996. View at Scopus
  10. J. R. Bach, C. Fuller, A. Gupta et al., “Virage image search engine: an open framework for image management,” in Storage and Retrieval for Still Image and Video Databases IV, Proceedings of SPIE, pp. 76–87, San Jose, Calif, USA, February 1996. View at Scopus
  11. X. Lin, B. Gokturk, B. Sumengen, and D. Vu, “Visual search engine for product images,” in Multimedia Content Access: Algorithms and Systems II, Proceedings of SPIE, January 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. taotaosou, 2011,
  13. C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point detectors,” International Journal of Computer Vision, vol. 37, no. 2, pp. 151–172, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Muller, W. Muller, D. M. Squire, S. Marchand-Maillet, and T. Pun, “Performance evaluation in content-based image retrieval: overview and proposals,” Pattern Recognition Letters, vol. 22, no. 5, pp. 593–601, 2001. View at Google Scholar
  15. N. Wei, G.-H. Geng, and M.-Q. Zhou, “An overview of performance evaluation in content-based image retrieval,” Journal of Image and Graphics, vol. 9, pp. 1271–1276, 2004. View at Google Scholar