Image interpretation using large corpus: Wikipedia

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Image interpretation using large corpus: Wikipedia
Authors: Mandar Rahurkar, Shen-Fu Tsai, Charlie Dagli, Thomas S. Huang [edit item]
Citation: Proceedings of the IEEE 98 (8): 1509-25. 2010.
Publication type: Journal article
Peer-reviewed: Yes
Database(s):
DOI: 10.1109/JPROC.2010.2050410.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Image interpretation using large corpus: Wikipedia is a publication by Mandar Rahurkar, Shen-Fu Tsai, Charlie Dagli, Thomas S. Huang.


[edit] Abstract

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.

[edit] Research questions

"In 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."

Research details

Topics: Multimedia information retrieval [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: Experiment responses, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Live Wikipedia [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: Not specified [edit item]

[edit] Conclusion

"In 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."

[edit] Comments


Further notes[edit]

Facts about "Image interpretation using large corpus: Wikipedia"RDF feed
AbstractImage 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 teamAdded on initial load +
Collected data time dimensionCross-sectional +
ConclusionIn 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 sourceExperiment responses + and Wikipedia pages +
Doi10.1109/JPROC.2010.2050410 +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Image%2Binterpretation%2Busing%2Blarge%2Bcorpus%3A%2BWikipedia%22 +
Has authorMandar Rahurkar +, Shen-Fu Tsai +, Charlie Dagli + and Thomas S. Huang +
Has domainComputer science +
Has topicMultimedia information retrieval +
Issue8 +
Pages1509-25 +
Peer reviewedYes +
Publication typeJournal article +
Published inProceedings of the IEEE +
Research designExperiment +
Research questionsIn 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.
Revid10,811 +
TheoriesUndetermined
Theory typeDesign and action +
TitleImage interpretation using large corpus: Wikipedia
Unit of analysisArticle +
Urlhttp://dx.doi.org/10.1109/JPROC.2010.2050410 +
Volume98 +
Wikipedia coverageSample data +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageNot specified +
Wikipedia page typeArticle +
Year2010 +