DC Field | Value | Language |
---|---|---|
dc.contributor.author | Min, H.-S. | ko |
dc.contributor.author | Lee, S. | ko |
dc.contributor.author | De Neve, W. | ko |
dc.contributor.author | Ro, YongMan | ko |
dc.date.accessioned | 2011-03-21T03:20:21Z | - |
dc.date.available | 2011-03-21T03:20:21Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2010-01-06 | - |
dc.identifier.citation | 16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010, pp.511 - 521 | - |
dc.identifier.uri | http://hdl.handle.net/10203/22820 | - |
dc.description.abstract | The automatic detection of semantic concepts is a key technology for enabling efficient and effective video content management. Conventional techniques for semantic concept detection in video content still suffer from several interrelated issues: the semantic gap, the Unbalanced data set problem, and a limited concept vocabulary size. In this paper, we propose to perform semantic concept detection for user-created video content using an image folksonomy in order to overcome the aforementioned problems. First, an image folksonomy contains a vast amount of user-contributed images. Second, a significant portion of these images has been manually annotated by users using a wide variety of tags. However, user-supplied annotations in an image folksonomy are often characterized by a high level of noise. Therefore, we also discuss a method that allows reducing the number of noisy tags in an image folksonomy. This tag refinement method makes use of tag co-occurrence statistics. To verify the effectiveness of the proposed video content annotation system, experiments were performed with user-created image and video content available on a number of social media applications. For the datasets used, video annotation with tag refinement has an average recall rate of 84% and an average precision of 75%, while video annotation without tag refinement shows an average recall rate of 78% and an average precision of 62%. | - |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | SPRINGER | - |
dc.title | Semantic concept detection for user-generated video content using a refined image folksonomy | - |
dc.type | Conference | - |
dc.identifier.wosid | 000279103200048 | - |
dc.identifier.scopusid | 2-s2.0-77249107819 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 511 | - |
dc.citation.endingpage | 521 | - |
dc.citation.publicationname | 16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010 | - |
dc.identifier.conferencecountry | CC | - |
dc.identifier.conferencelocation | Chongqing | - |
dc.identifier.doi | 10.1007/978-3-642-11301-7_51 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Ro, YongMan | - |
dc.contributor.nonIdAuthor | Min, H.-S. | - |
dc.contributor.nonIdAuthor | Lee, S. | - |
dc.contributor.nonIdAuthor | De Neve, W. | - |
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