Name
Abstract | ||
|---|---|---|
The tags on social media websites such as Flickr are frequently imprecise and incomplete, thus there is still a gap between these tags and the actual content of the images. This paper proposes a social image ``retagging'' scheme that aims at assigning images with better content descriptors. The refining process is formulated as an optimization framework based on the consistency between ``visual similarity'' and ``semantic similarity'' in social images. An effective iterative bound optimization algorithm is applied to learn the optimal tag assignment. In addition, as many tags are intrinsically not closely-related to the visual content of the images, we employ a knowledge-based method to differentiate visual content related from unrelated tags and then constrain the tagging vocabulary of our automatic algorithm within the content related tags. Experimental results on a Flickr image collection demonstrate the effectiveness of this approach. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1772690.1772848 | WWW |
Keywords | Field | DocType |
visual similarity,social media web,actual content,assigning image,better content descriptors,social image,visual content,flickr image collection,semantic consistency,automatic algorithm,content related tag,knowledge base,social media | Data mining,World Wide Web,Social media,Information retrieval,Computer science,Semantic consistency,Optimization algorithm,Social image,Vocabulary | Conference |
Citations | PageRank | References |
9 | 0.56 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Liu | 1 | 788 | 39.64 |
| Xian-Sheng Hua | 2 | 6566 | 328.17 |
| Meng Wang | 3 | 895 | 28.35 |
| Hong-Jiang ZHANG | 4 | 17378 | 1393.22 |