Nothing Special   »   [go: up one dir, main page]

Skip to main content

Automatic Generation of the Initial Query Set for CBIR on the Mobile Web

  • Conference paper
Advances in Multimedia Information Processing - PCM 2005 (PCM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3767))

Included in the following conference series:

Abstract

Despite the rapid growth of wallpaper image downloading service in the mobile contents market, users experience high levels of frustration in searching for desired images, due to the absence of intelligent searching aid. Although Content Based Image Retrieval is the most widely used technique for image retrieval in the PC-based system, its application in the mobile Web environment poses one major problem of not being able to satisfy its initial query requirement because of the limitations in user interfaces of the mobile application software. We propose a new approach, so called a CF-fronted CBIR, where Collaborative Filtering (CF) technique automatically generates a list of candidate images that can be used as an initial query in Content Based Image Retrieval (CBIR) by utilizing relevance information captured during Relevance Feedback. The results of the experiment using a PC-based prototype system verified that the proposed approach not only successfully satisfies the initial query requirement of CBIR in the mobile Web environment but also outperforms the current search process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Korea internet White Paper (2003)

    Google Scholar 

  2. Brunelli, R., Mich, O.: Image Retrieval by Examples. IEEE Transactions on Multimedia 2(3), 164–171 (2000)

    Article  Google Scholar 

  3. Cho, Y.H., Kim, J.K.: Application of Web Usage Mining and Product Taxonomy to Collaborative Recommendations in E-Commerce. Expert Systems with Applications 26(2), 233–246 (2004)

    Article  Google Scholar 

  4. Flickner, M., Sawhney, H., Niblack, W., et al.: Query by image and video content: The QBIC system. IEEE Computer Magazine 28(9), 23–32 (1995)

    Google Scholar 

  5. Kim, D.H., Chung, C.W., Barnard, K.: Relevance feedback using adaptive clustering for image similarity retrieval. Journal of Systems and Software 78(1), 9–23 (2005)

    Article  Google Scholar 

  6. Porkaew, K., Chakrabarti, K., Mehrotra, S.: Query Refinement for Multimedia Similarity Retrieval in MARS. In: Proc. 7th ACM Multimedia Conference, November 1999, pp. 235–238 (1999)

    Google Scholar 

  7. Sarwar, B., et al.: Analysis of Recommendation Algorithms for E-Commerce. In: Proc. ACM E-Commerce Conference, pp. 158–167 (2000)

    Google Scholar 

  8. Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating Word of Mouth. In: Proc. Conference on Human factors in Computing Systems, pp. 210–217 (1995)

    Google Scholar 

  9. Zhou, X.S., Huang, T.S.: Relevance feedback for image retrieval: a comprehensive review. ACM Multimedia Systems Journal 8(6), 536–544 (2003), 2

    Google Scholar 

  10. Wu, L., et al.: FALCON: Feedback Adaptive Loop for Content-Based Retrieval. In: Proc. 26th VLDB Conference, pp. 297–306 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, D.H., Kim, C.Y., Cho, Y.H. (2005). Automatic Generation of the Initial Query Set for CBIR on the Mobile Web. In: Ho, YS., Kim, H.J. (eds) Advances in Multimedia Information Processing - PCM 2005. PCM 2005. Lecture Notes in Computer Science, vol 3767. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11581772_84

Download citation

  • DOI: https://doi.org/10.1007/11581772_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30027-4

  • Online ISBN: 978-3-540-32130-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics