Advertisement

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

  • Deok Hwan Kim
  • Chan Young Kim
  • Yoon Ho Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3767)

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.

Keywords

Mobile Content Collaborative Filtering Content Based Image Retrieval Mobile Web Relevance Feedback 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Korea internet White Paper (2003)Google Scholar
  2. 2.
    Brunelli, R., Mich, O.: Image Retrieval by Examples. IEEE Transactions on Multimedia 2(3), 164–171 (2000)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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)CrossRefGoogle Scholar
  6. 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. 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. 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. 9.
    Zhou, X.S., Huang, T.S.: Relevance feedback for image retrieval: a comprehensive review. ACM Multimedia Systems Journal 8(6), 536–544 (2003), 2Google Scholar
  10. 10.
    Wu, L., et al.: FALCON: Feedback Adaptive Loop for Content-Based Retrieval. In: Proc. 26th VLDB Conference, pp. 297–306 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Deok Hwan Kim
    • 1
  • Chan Young Kim
    • 1
  • Yoon Ho Cho
    • 2
  1. 1.School of Computing & informationDongyang Technical CollegeSeoulKorea
  2. 2.School of e-BusinessKookmin UniversitySeoulKorea

Personalised recommendations