Search (21 results, page 1 of 2)

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Date

6. 1.2011 11:22:12

Type

a

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Abstract

This paper presents an overview of the nature and the characteristics of the numerous problems encountered when a user tries to access a collection of images in a multilingual environment. The authors identify major research questions to be investigated to improve image retrieval effectiveness in a multilingual environment.

Source

Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch

Type

a

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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.

Type

a

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Abstract

This paper reports on a research project that compared two different approaches for the indexing of ordinary images representing common objects: traditional indexing with controlled vocabulary and free indexing with uncontrolled vocabulary. We also compared image retrieval within two contexts: a monolingual context where the language of the query is the same as the indexing language and, secondly, a multilingual context where the language of the query is different from the indexing language. As a means of comparison in evaluating the performance of each indexing form, a simulation of the retrieval process involving 30 images was performed with 60 participants. A questionnaire was also submitted to participants in order to gather information with regard to the retrieval process and performance. The results of the retrieval simulation confirm that the retrieval is more effective and more satisfactory for the searcher when the images are indexed with the approach combining the controlled and uncontrolled vocabularies. The results also indicate that the indexing approach with controlled vocabulary is more efficient (queries needed to retrieve an image) than the uncontrolled vocabulary indexing approach. However, no significant differences in terms of temporal efficiency (time required to retrieve an image) was observed. Finally, the comparison of the two linguistic contexts reveal that the retrieval is more effective and more efficient (queries needed to retrieve an image) in the monolingual context rather than the multilingual context. Furthermore, image searchers are more satisfied when the retrieval is done in a monolingual context rather than a multilingual context.

Type

a

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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.

Type

a

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Abstract

There are currently two dominant interface types for searching and browsing large image collections: keywordbased search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.

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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.

Type

a

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Abstract

In large collections of images, one of the ways to facilitate browsing is by providing thumbnails of representative images. This paper seeks to examine the issue of choice of representative images within the categories. Towards this end, a study of free sorting of 50 images by 75 participants was conducted, in which they sorted the images into categories and selected a representative image for the categories and also indicated the prominent feature in the selected image. The results indicate that there is reasonable agreement in the choice of representative images and the selection of prominent features appearing in the images. The prominent feature seems to be one of the factors that have a bearing on the way people categorize.

Type

a

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Abstract

For many years, the Web became an important media for the diffusion of multilingual resources. Linguistic differenees still form a major obstacle to scientific, cultural, and educational exchange. Besides this linguistic diversity, a multitude of databases and collections now contain documents in various formats, which may also adversely affect the retrieval process. This paper describes a research project aiming to verify the existing relations between two indexing approaches: traditional image indexing recommending the use of controlled vocabularies or free image indexing using uncontrolled vocabulary, and their respective performance for image retrieval, in a multilingual context. This research also compares image retrieval within two contexts: a monolingual context where the language of the query is the same as the indexing language; and a multilingual context where the language of the query is different from the indexing language. This research will indicate whether one of these indexing approaches surpasses the other, in terms of effectiveness, efficiency, and satisfaction of the image searchers. This paper presents the context and the problem statement of the research project. The experiment carried out is also described, as well as the data collection methods

Type

a

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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).

Type

a

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Abstract

The aim of this paper was to analyze users' behavior during image retrieval exercises. Results revealed that users tend to follow a set search strategy: firstly they input one or two keyword search terms one after another and view the images generated by their initial search and after they navigate their way around the web by using the 'back to home' or 'previous page' buttons. These results are consistent with existing Web research. Many of the actions recorded revealed that subjects behavior differed depending on if the task set was presented as a closed or open task. In contrast no differences were found for the time subjects took to perform a single action or their use of the AND operator.

Type

a

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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.

Type

a

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Abstract

Faced with increasingly tight budgets many museums have come under pressure to explore new sources of income. The marketing of museum assets seems to offer one possibility especially since there is an apparently insatiable demand for content from the multimedia and 'edutainment' industries. Furthermore, providing access to the cultural heritage of Europe has come to be seen as a major aim of the EU and various programmes have been set up to encourage and facilitate this. Apart from the economic potential of digital museum content, the concomitant legal and security issues are investigated.

Type

a

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Abstract

To enable and guide effective metadata creation it is essential to understand the structure and patterns of the activities of the community around the photographs, resources used, and scale and quality of the socially created metadata relative to the metadata and knowledge already encoded in existing knowledge organization systems. This article presents an analysis of Flickr member discussions around the photographs of the Library of Congress photostream in Flickr. The article also reports on an analysis of the intrinsic and relational quality of the photostream tags relative to two knowledge organization systems: the Thesaurus for Graphic Materials (TGM) and the Library of Congress Subject Headings (LCSH). Thirty seven percent of the original tag set and 15.3% of the preprocessed set (after the removal of tags with fewer than three characters and URLs) were invalid or misspelled terms. Nouns, named entity terms, and complex terms constituted approximately 77% of the preprocessed set. More than a half of the photostream tags were not found in the TGM and LCSH, and more than a quarter of those terms were regular nouns and noun phrases. This suggests that these terms could be complimentary to more traditional methods of indexing using controlled vocabularies.

Type

a

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Abstract

This paper discusses the goals and results of the research project Perseus-a as an attempt to improve information retrieval of digital images by automatically connecting them with text-based descriptions. The development uses the image collection of prometheus, the distributed digital image archive for research and studies, the articles of the digitized Reallexikon zur Deutschen Kunstgeschichte, art historical terminological resources and classification data, and an open source system for linguistic and statistic automatic indexing called lingo.

Type

a

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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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