Search (6 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Bilder"
  • × year_i:[2000 TO 2010}
  1. Rorissa, A.: Relationships between perceived features and similarity of images : a test of Tversky's contrast model (2007) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.10, S.1401-1418
  2. Lee, C.-Y.; Soo, V.-W.: ¬The conflict detection and resolution in knowledge merging for image annotation (2006) 0.00
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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.
    Source
    Information processing and management. 42(2006) no.4, S.1030-1055
  3. Drolshagen, J.A.: Pictorial representation of quilts from the underground railroad (2005) 0.00
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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).
  4. Fukumoto, T.: ¬An analysis of image retrieval behavior for metadata type image database (2006) 0.00
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    Source
    Information processing and management. 42(2006) no.3, S.723-728
  5. Kim, C.-R.; Chung, C.-W.: XMage: An image retrieval method based on partial similarity (2006) 0.00
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    Source
    Information processing and management. 42(2006) no.2, S.484-502
  6. Ménard, E.: Image retrieval : a comparative study on the influence of indexing vocabularies (2009) 0.00
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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.