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MQSearch: image search by multi-class query

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Published:06 April 2008Publication History

ABSTRACT

Image search is becoming prevalent in web search as the number of digital photos grows exponentially on the internet. For a successful image search system, removing outliers in the top ranked results is a challenging task. Typical content based image search engines take an input image from one class as a query and compute relevance between the query and images in a database. The results often contain a large number of outliers, since these outliers may be similar to the query image in some way. In this paper we present a novel search scheme using query images from multiple classes. Instead of conducting query search for one image class at a time, we conduct multi-class query search jointly. By using several query classes that are similar to each other for multi-class query, we can utilize information across similar classes to fine tune the similarity measure to remove outliers. This strategy can be used for any information search application. In this work, we use content based image search to illustrate the concept.

References

  1. R. Albert, H. Jeong, and A. Barabsi. Diameter of the world wide web. Nature, 401:130--131, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  2. N. Boujemaa, F. Fleuret, V. Gouet, and H. Sahbi. Visual content extraction for automatic semantic annotation of video news. In the Proceedings of the SPIE Conference, San Jose, CA, 2004.Google ScholarGoogle Scholar
  3. M. Breitenbach and G. Grudic. Clustering through ranking on manifolds. In ICML, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Brin and L. Page. The anatomy of a large scale hypertextual web search engine. In WWW, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. I. Cox, M. Miller, T. Minka, and P. Yianilos. An optimized interaction strategy for bayesian relevance feedback. In IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. T. Gevers and A. Smeulders. Content-based image retrieval: An overview. In G. Medioni and S. B. Kang, editors, Emerging Topics in Computer Vision, Prentice Hall, 2004.Google ScholarGoogle Scholar
  7. J. He, M. Li, H. Zhang, H. Tong, and C. Zhang. Manifold-ranking based image retrieval. In ACM MM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Hein and M. Maier. Manifold denoising. In NIPS, 2006.Google ScholarGoogle Scholar
  9. Y. Rui, T. Huang, and S. Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. In Storage and Retrieval for Image and Video Databases (SPIE), pages 25--36, 1998.Google ScholarGoogle Scholar
  10. A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. on PAMI, 22(12):1349--1380, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Y. Zhao, Y. Zhao, and Z. Zhu. Relevance feedback based on query refining and feature database updating in cbir system. In Signal Processing, Pattern Recognition, and Applications, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Scholkopf. Ranking on data manifolds. In NIPS 16, 2003.Google ScholarGoogle Scholar
  13. X. Zhou and T. Huang. Relevance feedback in image retrieval: A comprehensive review. In IEEE CVPR Workshop on Content-based Access of Image and Video Libraries (CBAIVL), 2006.Google ScholarGoogle Scholar

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  1. MQSearch: image search by multi-class query

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                cover image ACM Conferences
                CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
                April 2008
                1870 pages
                ISBN:9781605580111
                DOI:10.1145/1357054

                Copyright © 2008 ACM

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 6 April 2008

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                CHI '08 Paper Acceptance Rate157of714submissions,22%Overall Acceptance Rate6,199of26,314submissions,24%

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