Content-Based Art Retrieval (C-BAR)

The prototype of an online Content-Based Art Retrieval (C-BAR) system is introduced that provides entrance to the digitized collection of the National Gallery of the Netherlands (the Rijksmuseum). The current online system of the Rijksmuseum is text-based and requires expert knowledge concerning the work searched for, else it fails in retrieving it. C-BAR extends this system with querying by an example image, which can be provided to the system or can be selected through browsing the collection. The global color distribution of the example image are extracted and compared with those of the images in the collection. Hence, based on either text or content-based features, the collection can be queried. Moreover, the matching process of C-BAR can be inspected. With the latter feature, C-BAR not only integrates the means to inspect collections by both experts and laypersons in one system but also provides the means to let the user to understand its working. These characteristics make C-BAR a unique system to access, enhance, and retrieve the knowledge available in digitized art collections.

[1]  J. Platt,et al.  National Association and Organization Reports. American Library Association; Association of American Publishers; American Booksellers Association; Association of Research Libraries; Scholarly Publishing and Academic Resources Coalition (SPARC); Council on Library and Information Resources. , 2003 .

[2]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Eduard Hoenkamp,et al.  Unitary Operators on the Document Spac , 2003, J. Assoc. Inf. Sci. Technol..

[4]  Toshikazu Kato,et al.  Database architecture for content-based image retrieval , 1992, Electronic Imaging.

[5]  Louis Vuurpijl,et al.  Vind(x): using the user through cooperative annotation , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[6]  Corinne Jörgensen,et al.  Access to Pictorial Material: A Review of Current Research and Future Prospects , 1999, Comput. Humanit..

[7]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[8]  Luciana Bordoni,et al.  Xml for libraries, archives, and museums: The project covax , 2003, Appl. Artif. Intell..

[9]  Louis Vuurpijl,et al.  New use for the pen: outline-based image queries , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[10]  Jennifer Trant Image Retrieval Benchmark Database Service: A Needs Assessment and Preliminary Develoment Plan , 2004 .

[11]  Louis G. Vuurpijl,et al.  Design guidelines for a Content-Based Image Retrieval color-selection interface , 2004 .

[12]  Louis G. Vuurpijl,et al.  Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions , 2005, Journal of Imaging Science and Technology.

[13]  Peter Stanchev,et al.  Content-Based Image Retrieval Systems , 2001 .