Skip to main content

Photo Retrieval from Personal Memories Using Generic Concepts

  • Conference paper
Advances in Multimedia Information Processing - PCM 2006 (PCM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4261))

Included in the following conference series:

Abstract

This paper presents techniques for retrieving photos from personal memories collections using generic concepts that the users specify. It is part of a larger project for capturing, storing, and retrieving personal memories in different contexts of use. Semantic concepts are obtained by training binary classifiers using the Regularized Least Squares Classifier (RLSC)and can be combined to express more complex concepts. The results that were obtained so far are quite good and by adding more low level features, better results are possible. The paper describes the proposed approach, the classifier and features, and the results that were obtained.

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

Access this chapter

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.

References

  1. Veltkamp, R., Tanase, M.: Content-Based Image Retrieval Systems: A Survey. Technical Report UU-CS-2000-34 (October 2000)

    Google Scholar 

  2. Hori, T., Aizawa, K.: Context-based video retrieval system for the life-log applications. In: Proceedings of the Fifth ACM SIGMM International Workshop on Multimedia Information Retrieval (Berkeley, CA), November 7, pp. 31–38. ACM Press, New York (2003)

    Chapter  Google Scholar 

  3. O’Hare, N., Jones, G., Gurrin, C., Smeaton, A.: Combination of content analysis and context features for digital photograph retrieval. In: IEE European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies, London (2005)

    Google Scholar 

  4. Lew, M., Sebe, N., Djeraba, C., Jain, R.: Content-based Multimedia Information Retrieval: State-of-the-art and Challenges. ACM Transactions on Multimedia Computing, Communication, and Applications 2(1) (2006)

    Google Scholar 

  5. Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, A.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  6. Mori, Y., Takahashi, H., Oka, R.: Image-to-word transformation based on dividing and vector quantizing images with words. In: Proceedings of the International Workshop on Multimedia Intelligent Storage and Retrieval Management (1999)

    Google Scholar 

  7. Yong, R., Huang, T., Mehrotra, S.: Relevance feedback techniques in interactive content-based image retrieval. In: Storage and Retrieval for Image and Video Databases (SPIE), pp. 25–36 (1998)

    Google Scholar 

  8. Zhou, X., Huang, T.: Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems 8(6), 536–544 (2003)

    Article  Google Scholar 

  9. Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notice of American Mathematical Society 50(5), 537–544 (2003)

    MATH  MathSciNet  Google Scholar 

  10. Jesus, R., Magalhães, J., Yavlinsky, A., Rüger, S.: Imperial College at TRECVID. TREC Video Retrieval Evaluation (TRECVID), Gaithersburg, MD (November 2005)

    Google Scholar 

  11. Jesus, R., Abrantes, A., Marques, J.: Relevance feedback in CBIR using the RLS classifier. In: 5th EURASIP Conference focused on Speech and Image Processing, Multimedia communications and Services, Bratislava, Junho (2005)

    Google Scholar 

  12. Wenyin, L., Sun, Y., Zhang, H.: MiAlbum-A System for Home Photo Management Using the Semi-Automatic Image Annotation Approach. ACM Multimedia (2000)

    Google Scholar 

  13. Wilhelm, A., Takhteyev, Y., Sarvas, R., Van House, N., Davis, M.: Photo Annotation on a Camera Phone. In: Proc. ACM CHI 2004, pp. 1403–1406 (2004)

    Google Scholar 

  14. World-Wide Media eXchange (2005), http://wwmx.org

  15. Cooper, M., Foote, J., Girgensohn, A.: Automatically organizing digital photographs using time and content. In: Proc. of the IEEE Intl. Conf. on Image Processing (ICIP 2003) (2003)

    Google Scholar 

  16. Jiebo, L., Boutell, M., Brown, C.: Pictures are not taken in a vacuum - an overview of exploiting context for semantic scene content understanding. Signal Processing Magazine, IEEE 23(2), 101–114 (2006)

    Article  Google Scholar 

  17. Jaimes, A.: Human Factors in Automatic Image Retrieval System Design and Evaluation. In: Proceedings of SPIE, Internet Imaging VII, San Jose, CA, USA, 6061 (2006)

    Google Scholar 

  18. Cusano, C., Ciocca, G., Schettini, R.: Image annotation using SVM. In: Proceedings of the SPIE, Internet Imaging V 2003, 5304, pp. 330–338 (2003)

    Google Scholar 

  19. Naphade, M.R., Huang, T.S.: A probabilistic framework for semantic video indexing, filtering, and retrieval. IEEE Transactions on Multimedia 3(1), 141–151 (2001)

    Article  Google Scholar 

  20. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Machine Intell. 18, 837–842 (1996)

    Article  Google Scholar 

  21. Platt, J.C.: Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: Smola, P.B.A., Schölkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (1999)

    Google Scholar 

  22. Correia, N., Alves, L., Correia, H., Morgado, C., Soares, L., Cunha, J., Romão, T., Dias, A.E., Jorge, J.: InStory: A System for Mobile Information Access, Storytelling and Gaming Activities in Physical Spaces. In: ACE 2005 - ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, Universidade Politècnica de Valencia (UPV), Spain, June 15-17 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jesus, R.M., Abrantes, A.J., Correia, N. (2006). Photo Retrieval from Personal Memories Using Generic Concepts. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_73

Download citation

  • DOI: https://doi.org/10.1007/11922162_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48766-1

  • Online ISBN: 978-3-540-48769-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics