Intuitive Large Image Database Browsing Using Perceptual Similarity Enriched by Crowds

  • Stefano Padilla
  • Fraser Halley
  • David A. Robb
  • Mike J. Chantler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)

Abstract

The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.

Keywords

Databases Images Navigation Browsers Perception Crowd Sourcing Similarity Retrieval Indexing Clustering Abstracts Textures 

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References

  1. 1.
    Clarke, A.D.F., Halley, F., Newell, A., Griffin, L., Chantler, M.J.: Perceptual similarity: a texture challenge. In: The 22nd British Machine Vision Conference, Dundee (2011)Google Scholar
  2. 2.
    Chen, J., Bouman, C.A., Dalton, J.: Hirachical Browsing and Search of Large Image Databases. IEEE Transactions on Image Processing, 442–455 (2000)Google Scholar
  3. 3.
    Combs, T.T.A., Bederson, B.B.: Does zooming improve image browsing? In: Proceedings of the Fourth ACM International Conference on Digital Libraries (1999)Google Scholar
  4. 4.
    Faria, F.F., Veloso, A., Almeida, H.M., Valle, E., da Torres, R.S., Gonzales, M.A., Meira Jr., W.: Learning to rank for content-based image retrieval. In: MIR 2010, pp. 285–294 (2010)Google Scholar
  5. 5.
    Heesch, D.: A survey of browsing models for content-based image retrieval. In: Multimedia Tools and Applications, vol. 40, pp. 261–284 (2008)Google Scholar
  6. 6.
    Holmquist, L.E.: Focus+context visualization with flip zooming and the zoom browser. In: CHI 1997 Extended Abstracts on Human Factors in Computer Systems, CHI EA 1997, pp. 263–264. ACM, New York (1997)Google Scholar
  7. 7.
    Krishnamachari, S., Abdel-Mottaleb, M.: Image browsing using hierarchical clustering. In: Proceedings IEEE International Symposium on Computers and Communications, pp. 301–307 (1999)Google Scholar
  8. 8.
    Lowe, D.G.: Perceptual Organization and Visual Recognition. Kluwer Acedemic Publishers, Norwell (1985)CrossRefGoogle Scholar
  9. 9.
    Martinez, J., Loisant, E.: Browsing image databases with galois’ lattices. In: Proceedings of the 2002 ACM Symposium on Applied Computing, SAC 2002, pp. 791–795. ACM, New York (2002)CrossRefGoogle Scholar
  10. 10.
    Pedronette, D.C.G., da Torres, R.S.: Exploring contextual information for image re-ranking. In: CIARP, pp. 514–548 (2010)Google Scholar
  11. 11.
    Perronmin, F., Liu, Y., Renders, J.M.: A family of contextual measures of similarity be-tween distributions with application to image retrieval. In: CVPR, pp. 2358–2365 (2009)Google Scholar
  12. 12.
    Pang, W.: An intuitive texture picker. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, UIU 2010, pp. 365–368. ACM, New York (2010)Google Scholar
  13. 13.
    Plant, W., Schaefer, G.: Visualisation and browsing of image databases. In: Lin, W., Tao, D., Kacprzyk, J., Li, Z., Izquierdo, E., Wang, H. (eds.) Multimedia Analysis, Processing and Communications. SCI, vol. 346, pp. 3–57. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Rao, A.R., Lohse, G.L.: Identifying high level features of texture perception. CVGIP. Graph. Models Image Processing 55, 218–233 (1993)CrossRefGoogle Scholar
  15. 15.
    Rodden, K.: How do people organize their photographs? In: Proceedings of the BCS IRSG Colloquium (1999)Google Scholar
  16. 16.
    Rodden, K., Basalaj, W., Sinclair, D., Wood, K.: Does organization by similarity assist image browsing? In: CHI 2001: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 190–197. ACM, New York (2001)CrossRefGoogle Scholar
  17. 17.
    Rogowiz, B.E., Frese, T., Smith, J.R., Bouman, C.E., Kalin, E.: Perceptual image similarity experiments. In: SPIE Conference on Human Vision and Electronic Imaging (1998)Google Scholar
  18. 18.
    Schaefer, G.: A next generation browsing environment for large image repositories. In: Multimedia Tools Applications, vol. 47, pp. 105–120 (2010)Google Scholar
  19. 19.
    Schwander, O., Nielsen, F.: Reranking with contextual dissimilarity measures from repre-sentational Bregman K-means. In: VISAPP, vol. 1, pp. 118–122 (2010)Google Scholar
  20. 20.
    Strong, G., Gong, M.: Browsing a large collection of community photos based on similarity on GPU. In: Bebis, G., et al. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 390–399. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in Matlab: the som toolbox. In: Proceeding of the Matlab DSP Conference, pp. 35–40 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefano Padilla
    • 1
  • Fraser Halley
    • 1
  • David A. Robb
    • 1
  • Mike J. Chantler
    • 1
  1. 1.The Texture Lab., School of Mathematical & Computer SciencesHeriot-Watt UniversityEdinburghUK

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