A study on video browsing strategies
A study on video browsing strategies
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bootstrapping approach to annotating large image collection
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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The well-built dataset is a pre-requisite for computer vision research. However, the process of collecting and labeling the images is laborious and monotonous. In this paper, we aim to automatic labeling and collecting the images for the visual object category. We propose an active learning approach in a co-training way. Our approaches starts from a small dataset with ground truth labels, and iteratively labels a larger set of unlabeled samples using the two irrelative classifiers and augment the labeled dataset, and update the learning model simultaneously. There are two advantages of our approach, one is to avoid drifting from the object category, and the other is to sequentially update the learning model with the increasing of the unlabeled samples. The experiment results demonstrate that our approach is effective and is superior to the self-training methods.