Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
To search or to label?: predicting the performance of search-based automatic image classifiers
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
Online multi-label active annotation: towards large-scale content-based video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning tag relevance by neighbor voting for social image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
On the sampling of web images for learning visual concept classifiers
Proceedings of the ACM International Conference on Image and Video Retrieval
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Social negative bootstrapping for visual categorization
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Multimedia Tools and Applications
Reliability and effectiveness of clickthrough data for automatic image annotation
Multimedia Tools and Applications
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Improving image tags by exploiting web search results
Multimedia Tools and Applications
| Hi-index | 0.00 |
Automatic visual categorization is critically dependent on labeled examples for supervised learning. As an alternative to traditional expert labeling, social-tagged multimedia is becoming a novel yet subjective and inaccurate source of learning examples. Different from existing work focusing on collecting positive examples, we study in this paper the potential of substituting social tagging for expert labeling for creating negative examples. We present an empirical study using 6.5 million Flickr photos as a source of social tagging. Our experiments on the PASCAL VOC challenge 2008 show that with a relative loss of only 4.3% in terms of mean average precision, expert-labeled negative examples can be completely replaced by social-tagged negative examples for consumer photo categorization.