A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotation via graph learning
Pattern Recognition
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Multi-label classification and extracting predicted class hierarchies
Pattern Recognition
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review on automatic image annotation techniques
Pattern Recognition
Multi-instance multi-label learning
Artificial Intelligence
Automated image annotation using global features and robust nonparametric density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Feature subset selection for efficient AdaBoost training
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
IEEE Transactions on Image Processing
Effective Semantic Annotation by Image-to-Concept Distribution Model
IEEE Transactions on Multimedia
Informative feature selection for object recognition via Sparse PCA
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Actively selecting annotations among objects and attributes
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Content-based annotation and classification framework: a general multi-purpose approach
Proceedings of the 17th International Database Engineering & Applications Symposium
Effective automatic image annotation via integrated discriminative and generative models
Information Sciences: an International Journal
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Image annotation tasks always lack accuracy and efficiency. Although many techniques that have been proposed in the last decade can give a reasonable performance, the large number of potential labels causes trouble in terms of decreasing the accuracy and efficiency. Both generative models and discriminative models have been proposed to solve the multi-label problem. Most of these complex models fail to achieve a good performance when they face an increasing number of image collections, with a dictionary that covers a large number of potential semantics. In this paper, we present a two-stage method for multi-class image labeling. We first introduce a simple label-filtering algorithm, which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then explore the relationship between the features to be used and each single class. Hence, specific and effective features will be selected for each class to form a label-specific classifier. In other words, our approach prunes specific features for each single label and formalizes the annotation task as a discriminative classification problem. Experiments prove that our two-stage framework can achieve both efficiency and accuracy for image annotation.