Name
Abstract | ||
|---|---|---|
Labeling human faces in images contained in Web media stories enables enriching the user experience offered by media sites. We propose a lightweight framework for automatic image annotation that exploits named entities mentioned in the article to significantly boost the accuracy of face recognition. While previous works in the area labor to train comprehensive offline visual models for a pre-defined universe of candidates, our approach models the people mentioned in a given story on the y, using a standard Web image search engine as an image sampling mechanism. We overcome multiple sources of noise introduced by this ad-hoc process, to build a fast and robust end-to-end system from off-the-shelf error-prone text analysis and machine vision components. In experiments conducted on approximately 900 faces depicted in 500 stories from a major celebrity news website, we were able to correctly label 81.5% of the faces while mislabeling 14.8% of them. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2187836.2187962 | WWW |
Keywords | Field | DocType |
face recognition,approach model,automatic image annotation,ad-hoc process,web media story,lightweight automatic face annotation,comprehensive offline visual model,media page,area labor,human face,media site,standard web image search,machine learning,user experience,text analysis,machine vision | User experience design,Machine vision,Computer science,Image retrieval,Artificial intelligence,Facial recognition system,World Wide Web,Annotation,Automatic image annotation,Information retrieval,Exploit,Machine learning,Image sampling | Conference |
Citations | PageRank | References |
1 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitri Perelman | 1 | 120 | 7.40 |
| Edward Bortnikov | 2 | 163 | 15.70 |
| Ronny Lempel | 3 | 1273 | 112.55 |
| Roman Sandler | 4 | 105 | 4.60 |