Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
International Journal of Computer Vision
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Discriminative fields for modeling semantic concepts in video
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
IEEE Transactions on Image Processing
Graph-based methods for the automatic annotation and retrieval of art prints
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Multi-kernel multi-label learning with max-margin concept network
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Labelset anchored subspace ensemble (LASE) for multi-label annotation
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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In this paper, a structured max-margin learning scheme is developed to achieve more effective training of a large number of inter-related classifiers for multi-label image annotation. First, a visual concept network is constructed to characterize the inter-concept visual similarity contexts more precisely and determine the inter-related learning tasks automatically. Second, multiple base kernels are combined to achieve more precise characterization of the diverse visual similarity contexts between the images and address the issue of huge intra-concept visual diversity more effectively. Third, a structured max-margin learning algorithm is developed by incorporating the visual concept network, max-margin Markov networks and multi-task learning to address the issue of huge inter-concept visual similarity more effectively. Our structured max-margin learning algorithm can leverage the inter-concept visual similarity contexts to learn a large number of inter-related classifiers simultaneously and improve their discrimination power significantly. Our experiments have also obtained very positive results.