Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Image Querying

  • Ilaria Bartolini
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1440

Synonyms

Image query processing

Definition

Image querying refers to the problem of finding, within image databases (Image DBs), objects that are relevant to a user query. Classical solutions to deal with such problem include the semantic-based approach, for which an image is represented through metadata (e.g., keywords), and the content-based solution, commonly called content-based image retrieval (CBIR), where the image content is represented by means of low-level features (e.g., color and texture). While, for the semantic-based approach, the image querying problem can be simply transformed into a traditional information retrieval problem, for CBIR more sophisticated query evaluation techniques are required. The usual approach to deal with this is illustrated in Fig. 1: By means of a graphical user interface (GUI), the user provides a query image, by sketching it using graphical tools, by uploading an image, or by selecting an image supplied by the system. Low-level features are...
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Ardizzoni S, Bartolini I, Patella M. Windsurf: region-based image retrieval using wavelets. In: Proceedings of the 1st International Workshop on Similarity Search; 1999. p. 167–73.Google Scholar
  2. 2.
    Bartolini I, Ciaccia P. Imagination: exploiting link analysis for accurate image annotation. In: Proceedings of the 5th International Workshop on Adaptive Multimedia Retrieval; 2007. p. 32–44.Google Scholar
  3. 3.
    Bartolini I, Ciaccia P. Scenique: a multimodal image retrieval interface. In: Proceedings of the 2008 International Working Conference on Advanced Visual Interfaces; 2008. p. 476–77.Google Scholar
  4. 4.
    Bartolini I, Ciaccia P, Oria V, Özsu T. Flexible integration of multimedia sub-queries with qualitative preferences. Multimed Tools Appl. 2007;33(3): 275–300.CrossRefGoogle Scholar
  5. 5.
    Bartolini I, Ciaccia P, Patella M. Query processing issues in region-based image databases. Knowl Inf Syst. 2010;25(2):389–420.CrossRefGoogle Scholar
  6. 6.
    Bartolini I, Patella M, Stromei G. Efficiently managing multimedia hierarchical data with the WINDSURF library. In: Communications in computer and information science, vol. 314/2012. Berlin/Heidelberg: Springer; 2012.CrossRefGoogle Scholar
  7. 7.
    Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robust features (SURF). Comput Vis Image Und. 2008;110(3):346–59.CrossRefGoogle Scholar
  8. 8.
    Carson C, Thomas M, Belongie S, Hellerstein JM, Malik J. Blobworld: a system for region-based image indexing and retrieval. In: Proceedings of the 3rd International Conference on Visual Information Systems; 1999. p. 509–16.CrossRefGoogle Scholar
  9. 9.
    Flickner M, Sawhney HS, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P. Query by image and video content: the QBIC system. IEEE Comput. 1995;28(9):23–32.CrossRefGoogle Scholar
  10. 10.
    Rubner Y, Tomasi C. Perceptual metrics for image database navigation. Boston: Kluwer Academic Publishers; 2000.zbMATHGoogle Scholar
  11. 11.
    Smeulders AWM, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell. 2000;22(12):1349–80.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering (DISI)University of BolognaBolognaItaly