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Image interpretation using large corpus: Wikipedia
Abstract Image is a powerful medium for expressing Image is a powerful medium for expressing one's ideas and rightly confirms the adage, One picture is worth a thousand words. In this work, we explore the application of world knowledge in the form of Wikipedia to achieve this objective-literally. In the first part, we disambiguate and rank semantic concepts associated with ambiguous keywords by exploiting link structure of articles in Wikipedia. In the second part, we explore an image representation in terms of keywords which reflect the semantic content of an image. Our approach is inspired by the desire to augment low-level image representation with massive amounts of world knowledge, to facilitate computer vision tasks like image retrieval based on this information. We represent an image as a weighted mixture of a predetermined set of concrete concepts whose definition has been agreed upon by a wide variety of audience. To achieve this objective, we use concepts defined by Wikipedia articles, e.g., sky, building, or automobile. An important advantage of our approach is availability of vast amounts of highly organized human knowledge in Wikipedia. Wikipedia evolves rapidly steadily increasing its breadth and depth over time.ncreasing its breadth and depth over time.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion In this work, we proposed, implemented, anIn this work, we proposed, implemented, and evaluated an algorithmfor expressing images as weighted representation of cognitive concepts to facilitate image understanding. We leveraged crowd sourced encyclopedia Wikipedia to learn basis representation for these concepts. Two fundamental algorithms were described: concept disambiguation and reranking that disambiguates and clarifies keywords, and image-to-concept mapping which acts as a bridge between visual feature and textual representation. These two parts are integral to image ranking systemwhich ranks images based on their semantic relevance to query keywords.heir semantic relevance to query keywords.
Data source Experiment responses  + , Wikipedia pages  +
Doi 10.1109/JPROC.2010.2050410 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Image%2Binterpretation%2Busing%2Blarge%2Bcorpus%3A%2BWikipedia%22  +
Has author Mandar Rahurkar + , Shen-Fu Tsai + , Charlie Dagli + , Thomas S. Huang +
Has domain Computer science +
Has topic Multimedia information retrieval +
Issue 8  +
Pages 1509-25  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in Proceedings of the IEEE +
Research design Experiment  +
Research questions In this work, we propose a unifying applicIn this work, we propose a unifying application that semantically sorts images with respect to the input text query. It is composed of keyword disambiguation and image-to-semantic-concept mapping. By mining structure and statistic of links among articles in Wikipedia, keywords that are article titles are disambiguated and ranked. As for image-to-semantic-concept mapping, after bag-ofwords image feature extraction [2], it is obtained by mining captions and locations of images in Wikipedia.ions and locations of images in Wikipedia.
Revid 10,811  +
Theories Undetermined
Theory type Design and action  +
Title Image interpretation using large corpus: Wikipedia
Unit of analysis Article  +
Url http://dx.doi.org/10.1109/JPROC.2010.2050410  +
Volume 98  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Live Wikipedia  +
Wikipedia language Not specified  +
Wikipedia page type Article  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:28:56  +
Categories Multimedia information retrieval  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:28:46  +
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