Semantic Web Dog Food

Presented at: 20th International World Wide Web Conference (WWW2011)

by Yan-Ying Chen, Winston H. Hsu, Hong-Yuan Mark Liao

Facial attributes such as gender, race, age, hair style, etc., carry rich information for locating designated persons and pro ling the communities from image/video collections (e.g., surveillance videos or photo albums). For plentiful facial attributes in photos and videos, collecting costly manual annotations for training detectors is time-consuming. We propose an automatic facial attribute detection method by exploiting the great amount of weakly labelled photos in social media. Our work can (1) automatically extract training images from the semantic-consistent user groups and (2) filter out noisy training photos by multiple mid-level features (by voting). Moreover, we introduce a method to harvest less-biased negative data for preventing uneven distribution of certain attributes. The experiments show that our approach can automatically acquire training photos for facial attributes and is on par with that by manual annotations.

Learning Facial Attributes by Crowdsourcing in Social Media was presented at this event.

Keywords: World Wide Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2011/poster/learning-facial-attributes-by-crowdsourcing-in-soc

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