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Metadata for Image Resources
Tomasz Neugebauer

July 2005

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Table of Contents

Introduction

The use of image resources is increasing, and so has the need to classify, store and provide efficient and innovative methods and technologies for their presentation, searching and retrieval by the users. The World Wide Web is an increasingly popular source of information, and this is a multimodal environment where image resources form a significant part of the communication medium.

Archives and museums that hold fragile materials such as special collections of rare books, architectural drawings, paintings, photographic prints, fashions, and sculptures, are increasingly looking into digitization of their collections. The technological progress in information technologies is making these digitization efforts financially feasible for the institutions, and an expectation on the part of the public. The benefits to the research community and the public of providing access to digital collections is obvious: researchers can use networking tools such as the World Wide Web to access information that was previously only available with expensive physical travel. Holding institutions benefit from the fulfillment of a cultural imperative while ensuring digital preservation and protection of the fragile originals from deterioration through use.

Metadata for Images

There are many metadata formats to choose from that can be used for organizing, describing and providing access to image collections: Visual Resources Association (VRA) Core Categories for Visual Resources[1], Dublin Core Metadata[2], USMARC Formats for Bibliographic Data[3], Encoded Archival Description (EAD)[4], Record Export for Art and Cultural Heritage (REACH) Element Set[5], Categories for Descriptions of Works of Art (CDWA)[6] and more. Organizations facing the decision as to which standard to adopt need to consider their own particular data set as well as the need to share their collections and metadata with other institutions.

This paper presents a summary of evaluation and analysis for metadata schemas for image collections, in search for evaluation standards for metadata schemas in this domain. The theoretical foundation for image metadata is provided, since the fact that we are dealing with the visual medium is one of the known variables and the metadata schema ought to adequately capture all of the possible types of information about an instance of this medium. The information needs of image seekers are explored since the metadata schema needs to provide a topology of access to these users. Both the interoperability and compatibility of metadata schema to content-based information is considered since multidisciplinary searching is an expectation on the part of image searchers who have become accustomed to searching heterogeneous sources on the World Wide Web.

VRA Core Categories

VRA Core Categories (version 3.0) “consist of a single element set that can be applied as many times as necessary to create records to describe works of visual culture as well as the images that document them” [7]. VRA Core is meant to facilitate interoperability among collections, while allowing for custom extensibility (adding new qualifiers) to capture detailed and custom collection information. Initially, VRA Core contained two separate element sets: one for the description of the work, and another for the description of the ‘visual document. These two element sets from VRA Core 2.0 were combined into a single universally applicable element set in version 3.0, and add the new element Record Type to distinguish the two types of (work or image) descriptions. The relationship between a work (‘a physical entity’) and an image (‘a visual representation of a work’) is one-to-one or one-to-many. There is overlap between VRA Core and Dublin Core (DC), for example “VRA Core 3 Relationship types are consistent with the DC Relation element”[8]. Another common principle between VRA Core and DC is the One-to-One Principle which states that each manifestation of a work should be treated as a separate object with metadata assigned to each manifestation, and the fact that all elements are optional.

Other Metadata Schemas

The USMARC Format for Bibliographic Data is usually combined with AACR2 for the most extensively used standard for representing and communicating bibliographic data in machine readable form. Another standard maintained by the Development and MARC Standards Office of the Library of Congress (LC) in partnership with the Society of American Archivists is the EAD Document Type Definition (DTD) for encoding archival finding aids using Extensible Markup Language (XML)[9]


    Record Export for Art and Cultural Heritage (REACH) Element Set was developed in 1997 as a part of a research project “to explore how existing information in museum collection management systems could be extracted and repurposed to provide online access to museum object descriptive information”[10]. Although this project is no longer active (site was ‘frozen’ in 2004), REACH element set does provide an example of a metadata schema for cultural heritage information objects. Similarly, version 2.0 of Categories for Descriptions of Works of Art (CDWA) is a schema for description of “the content of art databases by articulating a conceptual framework for describing and accessing information about objects and images”[11]. The important taxonomy for CDWA categories is that between information “intrinsic to the work (art object, architecture, or group) and information extrinsic to the work” such as “persons, places, and concepts related to the work may be important for retrieval, but is more efficiently recorded in separate authorities than in records about the work itself”[12].

Literature Review

Day presents the diverse nature of research in metadata for images with a detailed description of Dublin Core. (Day 1999) This diversity has resulted in proprietary image metadata formats maintained by many creating communities as well as more structured but simple generic formats such as Dublin Core, domain specific complex structures such as MARC and the components of “larger semantic frameworks” such as EAD. (Day 1999, 2) His description of Dublin Core emphasizes the role of this schema as a resource discovery tool. Day describes a number of projects relevant to integrating access to distributed collections: Consortium for the Computer Interchange of Museum Information (CIMI), Museum Educational Site Licensing Project (MESL), Electronic Library Image Service for Europe (ELISE), and Arts and Humanities Data Service (ASHDS) (Day 1999, 4-6). As a testament to the rapidly changing and complex nature of this field, searching for these initiatives online revealed that only ASHDS[13] has not ceased operations.

Greenberg performs a quantitative analysis of metadata schema that can be used for the various domains of image retrieval, including Dublin Core, VRA Core, REACH, and EAD (2001, 917-918). Greenberg’s analysis begins with a novel definition of metadata “structured data about data that supports discovery, use, authentication, and administration of information objects.” (2001, 917) The various metadata schemas were compared with regard to granularity and the distribution of types of elements into the four identified classes: discovery, use, authentication and administration. (Greenberg 2001, 918) The discovery class includes elements such as creator, title and subject and “assists in the identification and retrieval of an object.” (Greenberg 2001, 919) The use class “permits the technical and intellectual exploitation of an information object” and includes elements such as format, location, property rights and terms and conditions. (Greenberg 2001, 919) Authentication elements relate to legitimacy and integrity and include elements such as source, relationship and version. (Greenberg 2001, 919) Administration elements assist “with the management and custodial care of an object” and include provenance, and acquisition-related information including ownership (Greenberg 2001, 919). Greenberg finds that Dublin Core and REACH elements are 90% and over in the discovery class, EAD is the only non-discovery centered scheme favoring administrative elements. (2001, 919)

Marcia Lei Zeng applies the USMARC, VRA Core and Dublin Core to three dimensional realia, provides a comparison of these in the context of a museum fashion collection in particular and 3-D object descriptions in general. Among the challenges found are the following: the creator element (VRA Core and DC), 1XX field (authorship) and $c of 24X of USMARC are often uncertain or only partially known information about manufacturer is available (e.g. deduced from the language of inscription on the item). USMARC has fields 260 and 500 to capture manufacturer information whereas VRA Core and DC do not. (Zeng 1999, 1199) Similarly to Vercoustre & Paradis (1999), Zeng found that the title information was often difficult to determine due to lack of textual description from where it could be taken, making the title element of VRA Core, DC and USMARC field 24X a generic term. (Zeng 1999, 1199) For a theoretical foundation of the difference between generic and specific levels of description see Shatford (1986).

Chen (2001) uses various image retrieval tasks (e.g., describing, searching, sorting), including content-based retrieval (visual queries) to study search behaviour of art history students, and found significant relationships between textual queries (e.g., number of keywords participants planned to use) and search drawings (Chen 2001, 715). Jorgensen (1999) and Choi & Rasmussen (2003) offer good reviews of the literature in information needs and image queries. Jorgensen (1999) summarizes the relationship among types of queries and their associated attributes in the domains of: art history (creator, title, size, material, type, nationality, time period, technique and genre), visual content search (color, size, location, texture, shape, orientation), topical search (time, location, event or activity), event search (time, setting, activity), affective search (emotion or atmosphere), and conceptual search (abstract, symbolic, thematic, political, social, interpretive, state). (Jorgensen 1999, 311) As is evident from this list, both content-based attributes (e.g., color, size, texture, shape, etc.) and description-based attributes (e.g., time, setting, title, etc.) are searched for by the users. This suggests a need for closer collaboration between the “two distinctive research groups employing the content-based and description-based approaches, respectively.” (Chu 2001, 1017)

Kramer & Sesink (2003) report on the use of the XML based Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) along with the Safeguarding European Photographic Images for Access Data Element Set (SEPIADES). Kramer & Sesink (2003) note that although standards such as the International Standard for Bibliographic Description (ISBD) and MARC exist, “photographic archives are often not satisfied with the level of support these standards offer” (Kramer & Sesink 2003, 135). SEPIADES is the result of collaboration among the European Commission on Preservation and Access and various archives with the aim of producing “a complete metadata element set for the description of photographic objects.” (Kramer & Sesink 2003, 135). The reasoning for creating a distinct format for photographic materials includes the fact that, according to Kramer & Sesink (2003) all of the other formats are too general and so “a lot of descriptive information about the photograph and its content cannot be specified, or need to be specified in elements that were not defined for use with photographic information” (Kramer & Sesink 2003, 137).

Addis et al (2003) describe another large scale interoperability project (ARTISTE) between four major European galleries: the Uffizi in Florence, the National Gallery and the Victoria and Albert Museum in London, and the Centre de Recherche et de Restauration des Musées de France (C2RMF) (Addis et al. 2003, 91). The advantages of using the Resource Description Framework (RDF) over older protocols such as z39.50 [14] include the ability to specify image content metadata (as opposed to textual metadata only) using operators related to image content and methods “that result in the execution of image processing algorithms” (Addis et al 2003, 94).

Theoretical Foundations for Content Analysis of Images

The theoretical difficulties in creating metadata schemes for images as opposed to textual documents are related to the properties of the visual medium. Goodman argues that the visual medium does not posses the properties of a language such as “disjointness and differentiation” (Goodman 226). A symbol system is disjoint if an expression (i.e., a mark) can only validly belong to a single character (Goodman 133) and differentiated if it is theoretically possible to determine the character to which a mark belongs to (Goodman 136). Text has an advantage over the visual medium in that its syntax lends itself to analysis: the component words, sentences and paragraphs can be extracted from the content and mapped to a topology or ontology. The components and properties of images, on the other hand, can not be extracted with the same ease, for one, they are rarely explicitly labeled. It is meaningless to consider only the top 10 percent of an image (at least for a human), whereas it makes perfect sense to read the introduction of a paper. The non-linguistic ambiguity of the visual medium makes it difficult to understand what the classes, components and properties (e.g., relation to physical objects) of an image are.

Erwin Panofsky distinguishes between three categories of information in works of art in general: pre-iconography, iconography, and iconology. Pre-iconography functions at the descriptive level of basic generic objects and primary subject matter; iconography is an analytical class which requires knowledge of culture and convention; and iconology is a synthetic level that requires knowledge of art and criticism. (Choi & Rasmussen 2003, 499; Shatford 1986: 43) The pre-iconographic level is divided into the factual (e.g., window, flower, star) and expressional (e.g., anger, sadness, greed). All three levels contain Of and About facets for answers to the questions: who?, what?, when?, and where? (Shatford 1986: 43-53) Layne distinguishes between four facets of attributes that play a role in image retrieval: biographical, subject, exemplified and relationship (Layne 1994). Metadata schemes ought to be able to capture the whole spectrum of subject attributes, content related classes of terms, as well Layne’s biographical (creation and travel history), exemplified (e.g., photograph or poster) and relationship (e.g., preliminary drawing and finished painting) attributes.

User Needs

Jörgensen summarizes research suggesting that users search for objects in images at a ‘Basic Level’, which is “neither the most specific nor the most abstract level but is rather an intermediate level.” (Jörgensen 1999, 305) It is not possible to anticipate all of the possible reasons why someone is looking for an image, since the visual medium is particularly interdisciplinary. As Besser points out, a single image can be “useful to historians wanting a snapshot of the times, to architects looking at buildings, to urban planners looking at traffic patterns or building shadows, to cultural historians looking at changes in fashion, to medical researchers looking at female smoking habits, to sociologists looking at class distinctions, or to students looking at the use of certain photographic processes or techniques.” (Besser 1990, 788)

Users with diverse backgrounds and disciplines expect to be able to search for images across multiple sources for a plethora of attributes. Among the findings of Choi & Rasmussen (2003) is the fact that “most image content that the participants sought fitted into general person and thing, and event and condition limited by a geographic location or time period.” (Choi & Rasmussen 2003, 504-505)

Metadata Studies

Vercoustre & Paradis’ report on the use of Dublin Core as the metadata standard for an image CD-ROM application brings up some interesting observations (Vercoustre & Paradis 1999, 44). The publisher element is defined as “an entity responsible for making the resource available”[15], which in the case of the multimedia application is “the Association who supports the creation of the CD ROM” (Vercoustre & Paradis 1999, 44). However, the implication here is that if the image is ever to be reused by another application then this metadata element would have to be changed. The type element needed to be refined with an additional qualifier of quality in order to provide the application with the ability to select among ‘equivalent photos to display’ (Vercoustre & Paradis 1999, 44) Interestingly, the authors chose to add quality as a sub-element even though this “certainly [did] not conform to DC requirement for qualifier, but there was no appropriate place to add this information, using Dublin Core.” (Vercoustre & Paradis 1999, 44)

Zeng’s case study of USMARC, VRA Core and DC in a museum fashion collection suggests that USMARC notes (e.g., 500 general note, 508 creation/reproduction credit, 520 summary/abstract/annotation, 535 duplicate note, 541 acquisition, 561 provenance, 585 exhibition, and more) offer the highest level of differentiation among the many different kinds of notes that are necessary in capturing descriptive information such as item history, owner and person related to the item, significance of patters and colors, style history, exhibition records and conservation information, construction, design and composition (Zeng 1999, 1200). Another advantage of USMARC includes additional entries (6XX and 7XX) for persons and corporate bodies related to the item, providing additional access points for browsing, whereas VRA Core and DC lack this feature (Zeng 1999, 1200). VRA Core was found to differentiate well among some notes with elements such as: related work, relationship type, measurements, techniques, material, repository name and place, while leaving the rest (e.g., exhibition notes) to a generic ‘notes’ element, which does have an impact on searching capability for large collections (Zeng 1999, 1200) and interoperability. However, VRA Core offers superior detail to DC and USMARC in differentiating subject access elements with the introduction of additional elements such as nationality/culture, style/period/group/movement (Zeng 1999, 1202). Whereas DC was the quickest solution, offering minimal cataloguing time, USMARC required the most skill and time (Zeng 1999, 1205) In general, a slightly modified VRA Core was found to be the most suitable for the fashion image collection (Zeng 1999, 1205).

Safeguarding European Photographic Images for Access Data Element Set (SEPIADES) is a complete element set for description of photographic objects with a structure describing all aspects of the photographic resource and a “hierarchical structure in which the resources were categorized by the archive” (Kramer & Sesink 2003, 136). This type of hierarchical structure which “gives the users searching a repository the possibility to browse through an archives collections or search for collections rather than just one image” (Kramer & Sesink 2003, 136) seems like a similar structure to the EAD DTD. The SEPIADES hierarchy consists of: institute, acquisition, collection, grouping, visual image (what can be seen on the photograph) and physical image (instances of the image such as negative or print) (Kramer & Sesink 2003, 136). There is a similarity between the separation of work and image in VRA Core and SEPIADES distinction between visual and physical image.

Interoperability

The Consortium for the Computer Interchange of Museum Information (CIMI) ceased operations. The responsibility over the CIMI XML Schema for description of museum objects was transferred to the organization which is responsible for the SPECTRUM standard upon which it is based (MDA).[16] The CIMI XML Schema is useful for “migrating data, the sharing of information between applications, and as an interchange format of OAI (Open Archives Initiative) metadata harvesting” [17]

In their proposal for an interoperable framework for description of photographs using the element set SEPIADES, Kremer & Sesink (2003) point out that photographic archives “tend to add or remove elements from these standards and in doing so invalidate compatibility with other standard implementations.” (Kremer & Sesink 2003, 135) The variability of collections and complexity and difficulty in analysis of the visual medium suggests that heterogeneous metadata standards will continue to exist for the increasing number of digitalized archives. Initiatives such as the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), implemented with the help of Extensible Markup Language (XML) will continue to gain importance for their ability to provide search functionality across different archives: “data providers offer their metadata to automatic harvesters run by service providers [that in turn] offer indexing capabilities and retrieval of the harvested metadata records” (Kremer & Sesink 2003, 137).

Resource Description Framework (RDF) is another XML-compatible common framework (parsers and processing tools) intended to facilitate the interoperability of metadata between applications[18]. Reporting on the retrieval of image content through metadata searching, Addis et al (2003) use RDF in ARTISTE extensively to provide a “way to establish a common semantics between heterogeneous digital libraries containing image collections.” (Addis et al 2003, 95). Like OAI-PMH, RDF is open to accept all kinds of property vocabularies (XML Namespaces), and as such offers a platform for integrating large collections of metadata on the web through web applications into searchable environments.

Suggestions and Implications

VRA Core can be supplemented with additional elements, and Zeng offers some notable addition suggestions, including secondary materials, provenance, citations, conservation and structure (Zeng 1999, 1203-1204). The minimal and generic nature of VRA Core and Dublin Core raises questions about the utility of these standards in specialized image collections. Extending these element sets as needed by individual institutions creates serious challenges for interoperability.

There are a number of thesauri and controlled vocabularies for visual information such as the Art and Architecture Thesaurus which uses hierarchically arranged “terminology describing physical attributes, styles and periods, agents, activities, materials and objects” and ICONCLASS, a visual classification system used in art history (Baxter & Anderson). In addition to this, there are controlled vocabularies for names such as Union List of Artists Names (ULAN)[19] and Getty Thesaurus of Geographic Names (TGN)[20]. Any metadata scheme for images ought to allow for the explicit referencing of these specialized vocabularies for images so that interoperability is not compromised at the level of the schema.

The hierarchical SEPIADES scheme for photographs seems similar to the EAD DTD, but has the advantage of being part of a framework that includes the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). A comparative study of the EAD DTD and SEPIADES would be a useful step towards ensuring essential interoperability between EAD and OAI-PMH.

The Resource Description Framework (RDF) and Open Archive Initiative Protocol for Metadata Harvesting seem to have the same goals: metadata interoperability. An investigation into similarity and interoperability of these two standards for their strengths and weaknesses could be the start of a new system that integrates the best features and compatibilities of both.

ISO/IEC JTC1 SC32 WG2 is the Working Group that develops international standards for metadata and related technologies [21] within the framework of the International Standards Organization (ISO). For example, the ISO/IEC 11179 series of standards describing “data framework, classification, registry metamodel and basic attributes, data definition, naming and identification, and registration” [22] should be consulted by metadata schema and framework developers. The success of Dublin Core (DC) is at least in part due to the fact that DC is structured according to ISO standards: “Each Dublin Core element is defined using a set of ten attributes from the ISO/IEC 11179 [ISO11179] standard for the description of data elements.” [23]

Conclusion

This paper presents a review of the various metadata schemas, organizations, initiatives and retrieval applications in the area of image collections. The number of different standards available for images is large, and both the research and applications seem to be accelerating in this area. The fast-pace of progress seems to have the drawback of quick obsolescence of many initiatives and working groups.

The theoretical foundation for some of the difficulties in the analysis of the visual medium suggests that the plethora of schemas in specific domains will continue to be a reality. For this reason, the image-metadata community is wise to focus on interoperability technologies such as the Resource Description Framework (RDF), Extensible Markup Language (XML) and compatibility with minimal metadata sets such as Dublin Core. Due to the ambiguity and complexity of visual information, it seems unlikely that we will find a single ideal metadata scheme for visual materials of all kinds. The most obvious candidate for this job would be the MARC standard, but studies show that archives and special image collections are not using MARC for reasons of efficiency and expressivity.

I recommend further comparative studies between the various schemas and technologies that currently exist, especially with the aim of integrating them into a common framework such as the Open Archive Initiative. I also recommend paying attention to the International Standards Organization (ISO) for principles of development of metadata schema.

Works Cited:

Addis, Matthew, Boniface, Mike, Goodall, S., Grimwood, Paul, Kim, Sanghee, Lewis, Paul, Martinez, Kirk, Stevenson, Alison. “Integrated Image Content and Metadata Search and Retrieval across Multiple Databases”. International Conference on Image and Video Retrieval (CIVR) 2003. 3 Apr. 2005. < http://eprints.ecs.soton.ac.uk/8915/01/civr_photoready2.pdf >

Baxter, Graeme. Anderson, Douglas. “Image indexing and retrieval: some problems and proposed solutions”. Internet Research 6.4 (1996).

Besser, H. “Visual Access to Visual Images: The UC Berkley Image Database Project”. Library Trends 38.4 (1990): 787-98.

Chu, H. “Research in Image Indexing and Retrieval as Reflected in the Literature.” Journal of the American Society for Information Science and Technology 52.12 (2001): 1011-1018.

Chen, Hsin-liang. “An analysis of image retrieval tasks in the field of art history.” Information Processing and Management. 37 (2001): 701-720.

Choi, Youngok & Rasmussen, Edie, M. “Searching for Images: The Analysis of Users’ Queries for Image Retrieval in American History”. Journal of the American Society for Information Science and Technology 54.6 (2003): 498-511.

Erwin Panofsky, Studies in Iconology: Humanistic Themes in the Art of the Renaissance. New York: Harper & Row, 1962.

Day, Michael. “Metadata for images: emerging practice and standards.” Proc. the Challenge of Image Retrieval: CIR 99 – Second UK Conference on Image Retrieval, Feb 1999. 5 Apr. 2005. < http://ewic.bcs.org/conferences/1999/imageret/papers/paper4.pdf >

Goodman, Nelson. Languages of Art : an approach to a theory of symbols. Indianapolis: Hackett Publishing Company, 1976.

Greenberg, Jane. “A Quantitative Categorical Analysis of Metadata Elements in Image-Applicable Metadata Schemas.” Journal of the American Society for Information Science and Technology 52.11 (2001): 917-924.

Jörgensen, Corinne. “Access to Pictorial Material: A Review of Current Research and Future Prospects.” Computers and Humanities 33 (1999): 293-318.

Kramer, Rutger & Sesink, Laurents. “Framework for Photographic Archives Interoperability”. 2003. SIIT 2003 Conference Proceedings.

Shatford Layne, S. "Some issues in the indexing of images." Journal of the American Society for Information Science 45.8 (1994): 583-588.

Vercoustre, Anne-Marie & Paradis, François. "Metadata for Photographs: From Digital Library to Multimedia Application". Third European Conference on Digital Libraries (ECDL’99) LNCS 1696: 39-57 (1999).

Zeng, Marcia Lei. “Metadata Elements for Object Description and Representation: A Case Report from a Digitized Historical Fashion Collection Project.” Journal of the American Society for Information Science 50.13 (1999): 1193-1208.



[1] VRA Core Categories, Version 3.0. 20 Feb. 2002. Visual Resources Association Data Standards Committee. 26 March 2005. <http://www.vraweb.org/vracore3.htm>

[2] Dublin Core Metadata Initiative. 2005. DCMCI. 26 March 2005 <http://dublincore.org/>

[3] Marc 21 Concise Format for Bibliographic Data. 2 Sep. 2005. Library of Congress Network Development and MARC Standards Office. 1 April 2005. <http://www.loc.gov/marc/bibliographic/ecbdhome.html>

[4] Encoded Archival Descriptions EAD. 18 March 2005. Library of Congress. 1 April 2005. <http://www.loc.gov/ead/>

[5] RLG REACH Element Set for Shared Description of Museum Objects. 2 Sep. 1998. RLG. 1 April 2005 .<http://www.rlg.org/reach.elements.html> (“site was frozen on 29 March 2004”)

[6] Categories for the Description of Works of Art. 20 Sep. 2000. The J. Paul Getty Trust & College Art Association 1 Apr. 2005. <http://www.getty.edu/research/conducting_research/standards/cdwa/index.html>

[7]VRA Core Categories, Version 3.0. 20 Feb. 2002. Visual Resources Association Data Standards Committee. 1 Apr. 2005. <http://www.vraweb.org/vracore3.htm>

[8] VRA Core Categories, Version 3.0. 20 Feb. 2002. Visual Resources Association Data Standards Committee. 1 Apr. 2005. <http://www.vraweb.org/vracore3.htm>

[9] Encoded Archival Descriptions EAD. 18 March 2005. Library of Congress. 1 April 2005. <http://www.loc.gov/ead/>

[10] RLG REACH Element Set for Shared Description of Museum Objects. 2 Sep. 1998. RLG. 1 April 2005. <http://www.rlg.org/reach.elements.html>

[11] Categories for the Description of Works of Art - Introduction. 20 Sep. 2000. The J. Paul Getty Trust & College Art Association 1 Apr. 2005. <http://www.getty.edu/research/conducting_research/standards/cdwa/index.html>

[12] Categories for the Description of Works of Art - Introduction. 20 Sep. 2000. The J. Paul Getty Trust & College Art Association 1 Apr. 2005. <http://www.getty.edu/research/conducting_research/standards/cdwa/index.html>

[13] Arts and Humanities Data Service (AHDS). 31 Mar. 2005. AHDS. 2 April 2005. <http://ahds.ac.uk/>

[14] Z39.50 International Standards Maintenance Page. 21 Mar. 2005. Library of Congress Network Development and MARC Standards Office. 2 Apr. 2005 <http://www.loc.gov/z3950/agency/>

[15] Dublin Core Metadata Element Set, Version 1.1: Reference Description – Publisher. 20 Dec. 2004. DCMI. 2 Apr. 2005. <http://dublincore.org/documents/dces/>

[16] CIMI XML Working Group 17 Dec. 2003. CIMI. 4 Apr. 2005. <http://www.cimi.org/wg/xml_spectrum/index.html>

[17] Ibid.

[18] RDF Primer 10 Feb. 2004. World Wide Web Consortium. 17 Mar. 2005, <http://www.w3.org/TR/rdf-primer/>

[19] Getty Union List of Artist Names. 2004. J. Paul Getty Trust. 5 Apr. 2005. <http://www.getty.edu/research/conducting_research/vocabularies/ulan/>

[20] Getty Thesaurus of Geographic Names. 2004. J. Paul Getty Trust. 5 Apr. 2005. <http://www.getty.edu/research/conducting_research/vocabularies/tgn/>

[21] SC32 WG2 Metadata Standards Home Page. 5 Apr. 2005. International Organization for Standardization & International Electrotechnical Commission JTC1. 6 Apr. 2005. <http://metadata-standards.org/>

[22] ISO/IEC 11179, Information Technology -- Metadata Registries (MDR). 25 Mar. 2005. International Organization for Standardization & International Electrotechnical Commission JTC1. 6 Apr. 2005. <http://metadata-standards.org/11179/>

[23] Dublin Core Metadata Element Set, Version 1.1: Reference Description. 2 Jul. 1999. DCMI. 5 April 2005. <http://dublincore.org/documents/1999/07/02/dces/>


Metadata for Image Resources

by: Tomasz Neugebauer

July 2005



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