Paper Info
Title
Automatic generation of semantic fields for annotating web images
Abstract
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 user-supplied 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 NUS-WIDE web image corpus and demonstrate superior performance on image annotation as compared to the state-of-the-art approaches.
Year
Venue
Keywords
2010
COLING (Posters)
different corpus,semantic field,annotating web image,semantic fields,image annotation,automatic generation,target concept,nus-wide web image corpus,web image,high tag co-occurrences,image corpus,semantic concept
Field
DocType
Volume
Automatic image annotation,Information retrieval,Semantic Web Stack,Computer science,Image retrieval,Artificial intelligence,Natural language processing,Social Semantic Web,Semantic field,WordNet,Web image
Conference
C10-2
Citations 
PageRank 
References 
1
0.36
17
Authors
4
Name
Order
Citations
PageRank
Gang Wang128265.93
Tat-Seng Chua211749653.09
C. W. Ngo34271211.46
Yong Cheng Wang410.70