Search (8 results, page 1 of 1)

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Abstract

The Underground Railroad was a network of people who helped fugitive slaves escape to the North and Canada during the U.S. Civil War period, beginning in about 1831. Quilting was used as a form of information representation (Breneman 2001). This simple classification was designed to relate the symbolic transmission of escape routes and locations of sanctuary. Because it was for use in a children's library, symbolic representations were used to anchor the classes. Symbols are based in the African graphic arts, the Adinkra symbols of Ghana (West African wisdom. 2001), and also from actual quilt practice (Threads of Freedom 2001 and Breneman 2001).

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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

The rapid growth of the numbers of images and their users as a result of the reduction in cost and increase in efficiency of the creation, storage, manipulation, and transmission of images poses challenges to those who organize and provide access to images. One of these challenges is similarity matching, a key component of current content-based image retrieval systems. Similarity matching often is implemented through similarity measures based on geometric models of similarity whose metric axioms are not satisfied by human similarity judgment data. This study is significant in that it is among the first known to test Tversky's contrast model, which equates the degree of similarity of two stimuli to a linear combination of their common and distinctive features, in the context of image representation and retrieval. Data were collected from 150 participants who performed an image description and a similarity judgment task. Structural equation modeling, correlation, and regression analyses confirmed the relationships between perceived features and similarity of objects hypothesized by Tversky. The results hold implications for future research that will attempt to further test the contrast model and assist designers of image organization and retrieval systems by pointing toward alternative document representations and similarity measures that more closely match human similarity judgments.

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Abstract

XMage is introduced in this paper as a method for partial similarity searching in image databases. Region-based image retrieval is a method of retrieving partially similar images. It has been proposed as a way to accurately process queries in an image database. In region-based image retrieval, region matching is indispensable for computing the partial similarity between two images because the query processing is based upon regions instead of the entire image. A naive method of region matching is a sequential comparison between regions, which causes severe overhead and deteriorates the performance of query processing. In this paper, a new image contents representation, called Condensed eXtended Histogram (CXHistogram), is presented in conjunction with a well-defined distance function CXSim() on the CX-Histogram. The CXSim() is a new image-to-image similarity measure to compute the partial similarity between two images. It achieves the effect of comparing regions of two images by simply comparing the two images. The CXSim() reduces query space by pruning irrelevant images, and it is used as a filtering function before sequential scanning. Extensive experiments were performed on real image data to evaluate XMage. It provides a significant pruning of irrelevant images with no false dismissals. As a consequence, it achieves up to 5.9-fold speed-up in search over the R*-tree search followed by sequential scanning.

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Date

26.12.2007 18:29:29

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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