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Abstract

Semantic annotation of images is an important step to support semantic information extraction and retrieval. However, in a multi-annotator environment, various types of conflicts such as converting, merging, and inference conflicts could arise during the annotation. We devised conflict detection patterns based on different data, ontology at different inference levels and proposed the corresponding automatic conflict resolution strategies. We also constructed a simple annotator model to decide whether to trust a given piece of annotation from a given annotator. Finally, we conducted experiments to compare the performance of the automatic conflict resolution approaches during the annotation of images in the celebrity domain by 62 annotators. The experiments showed that the proposed method improved 3/4 annotation accuracy with respect to a naïve annotation system.

Date

26.12.2007 18:29:29

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Abstract

This paper is a current assessment of the status of metadata creation and mapping between catalogerdefined field names and Dublin Core (DC) metadata elements across three digital image collections. The metadata elements that evince the most frequently inaccurate, inconsistent and incomplete DC metadata application are identified. As well, the most frequently occurring locally added metadata elements and associated pattern development are examined. For this, a randomly collected sample of 659 metadata item records from three digital image collections is analyzed. Implications and issues drawn from the evaluation of the current status of metadata creation and mapping are also discussed in relation to the issue of semantic interoperability of concept representation across digital image collections. The findings of the study suggest that conceptual ambiguities and semantic overlaps inherent among some DC metadata elements hinder semantic interoperability. The DC metadata scheme needs to be refined in order to disambiguate semantic relations of certain DC metadata elements that present semantic overlaps and conceptual ambiguities between element names and their corresponding definitions. The findings of the study also suggest that the development of mediation mechanisms such as concept networks that facilitate the metadata creation and mapping process are critically needed for enhancing metadata quality.

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Abstract

Web 2.0 and social/collaborative tagging have altered the traditional roles of indexer and user. Traditional indexing tools and systems assume the top-down approach to indexing in which a trained professional is responsible for assigning index terms to information sources with a potential user in mind. However, in today's Web, end users create, organize, index, and search for images and other information sources through social tagging and other collaborative activities. One of the impediments to user-centered indexing had been the cost of soliciting user-generated index terms or tags. Social tagging of images such as those on Flickr, an online photo management and sharing application, presents an opportunity that can be seized by designers of indexing tools and systems to bridge the semantic gap between indexer terms and user vocabularies. Empirical research on the differences and similarities between user-generated tags and index terms based on controlled vocabularies has the potential to inform future design of image indexing tools and systems. Toward this end, a random sample of Flickr images and the tags assigned to them were content analyzed and compared with another sample of index terms from a general image collection using established frameworks for image attributes and contents. The results show that there is a fundamental difference between the types of tags and types of index terms used. In light of this, implications for research into and design of user-centered image indexing tools and systems are discussed.

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Footnote

Rez. in: Frankfurter Rundschau. Nr.298 vom 23.12.2010, S.29 (Scalla, M.: Bilder sehen Dich an).

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Source

Frankfurter Rundschau. Nr.298 vom 23.12.2010, S.29

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Date

6. 1.2011 11:22:12