Automatic generation of semantic fields for annotating web images
|Automatic generation of semantic fields for annotating web images|
|Author(s)||Wang G., Chua T.S., Ngo C.-W., Wang Y.C.|
|Published in||Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Automatic Generation, Image annotation, Media content, Multimedia contents, Photo sharing, Semantic concept, Semantic fields, Target concept, Web images, Wikipedia, Wordnet, Computational linguistics, Semantics, Software agents, User interfaces, Websites, Semantic Web)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Automatic generation of semantic fields for annotating web images is a 2010 conference paper written in English by Wang G., Chua T.S., Ngo C.-W., Wang Y.C. and published in Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference.
The overwhelming amounts of multimedia contents have triggered the need for automatically detecting the semantic concepts within the media contents. With the development of photo sharing websites such as Flickr, we are able to obtain millions of images with usersupplied tags. However, user tags tend to be noisy, ambiguous and incomplete. In order to improve the quality of tags to annotate web images, we propose an approach to build Semantic Fields for annotating the web images. The main idea is that the images are more likely to be relevant to a given concept, if several tags to the image belong to the same Semantic Field as the target concept. Semantic Fields are determined by a set of highly semantically associated terms with high tag co-occurrences in the image corpus and in different corpora and lexica such as WordNet and Wikipedia. We conduct experiments on the NUSWIDE web image corpus and demonstrate superior performance on image annotation as compared to the state-ofthe- art approaches.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.