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Image Database

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Synonyms

Content-based image retrieval (CBIR); Image retrieval; Image retrieval system

Definition

Given a collection of images, a full-fledged image database provides means and technologies that support an efficient and rich modeling, storing, indexing, retrieval, and manipulation of images and its metadata. The modeling of images can range, depending on the used metadata format (e.g., MPEG-7), from simple technical annotations such as file size, creator, etc., to more sophisticated annotations such as low-level features (e.g., color) or even high-level features (e.g., objects, events, etc.). The storing component is responsible for mapping the used metadata format to an adequate database schema. Indexing facilities should support efficient retrieval and need to provide means (depending on the used metadata) for indexing text, multidimensional feature vectors, and high-level representations. The retrieval and query specification should support some or all of the following concepts:...

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Recommended Reading

  1. Chang NS, Fu KS. Query by pictorial example. IEEE Trans Softw Eng. 1980;6(6):519–24.

    Article  Google Scholar 

  2. Tamura H, Yokoya N. Image database systems: a survey. Pattern Recogn. 1984;17(1):29–43.

    Article  Google Scholar 

  3. Shatford S. Analyzing the subject of a picture: a theoretical approach. Cat Classif Q. 1986;6(3):39–62.

    Google Scholar 

  4. Gaede V, Günther O. Multidimensional access methods. ACM Comput Surv. 1998;30(2):170–231.

    Article  Google Scholar 

  5. Veltkamp RC, Tanase M. Content-based image retrieval systems: a survey, technical report. The Netherlands: Utrecht University; 2000.

    Google Scholar 

  6. 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.

    Article  Google Scholar 

  7. Gupta A, Weymouth T, Jain R. Semantic queries with picture: the VIMSYS model. In: Proceedings of the 17th Conference on Very Large Databases; 1991. p. 69–79.

    Google Scholar 

  8. Besufekad SA. Modélisation et traitement de requêtes images complexes, PhD-thesis, L’Institut National des Sciences Appliquées de Lyon, 2003.

    Google Scholar 

  9. Wang F, Kong J, Cooper L, Pan T, Kurc T, Chen W, Sharma A, Niedermayr C, Oh TW, Brat D, Farris AB, Foran DJ, Saltz J. A data model and database for high-resolution pathology analytical image informatics. J Pathol Inform. 2011;2(1):32.

    Article  Google Scholar 

  10. Christopher JC. Burges, dimension reduction: a guided tour. Mach Learn. 2010;2(4):275–365.

    Google Scholar 

  11. Liuz Y, Cuiz J, Huangx Z, Liz H, Shen HT. SKLSH: an efficient index structure for approximate nearest neighbor search. Proc VLDB Endowment. 2014;7(9):745–56.

    Article  Google Scholar 

  12. Lee J-H, Cha G-H, Chung C-W. A model for k-nearest neighbor query processing cost in multidimensional data spaces. Inf Process Lett. 1999;69(2):69–76.

    Article  MathSciNet  MATH  Google Scholar 

  13. Kim K, Hasan MK, Heo J-P, Tai Y-W, Yoon S-e. Probabilistic cost model for nearest neighbor search in image retrieval. Comput Vis Image Underst. 2012;116(9):991–8.

    Article  Google Scholar 

  14. Melton J, Eisenberg A. SQL multimedia application packages (SQL/MM). ACM SIGMOD Rec. 2001;30(4):97–102.

    Article  Google Scholar 

  15. Mario Döller, Ruben Tous, Frederik Temmermans, Kyoungro Yoon, Je-Ho Park, Youngseop Kim, Florian Stegmaier und Jaime Delgado. JPEG’s JPSearch standard: harmonizing image management and search. IEEE MultiMed. 2013; 20(4):38–48.

    Article  Google Scholar 

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Correspondence to Mario Döller .

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Döller, M., Kosch, H. (2018). Image Database. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1007

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