Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Convex Optimization
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
Learning instance specific distances using metric propagation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance metric learning from uncertain side information with application to automated photo tagging
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Learning semantic distance from community-tagged media collection
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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The semantic contextual information is shown to be an important resource for improving the scene and image recognition, but is seldom explored in the literature of previous distance metric learning (DML) for images. In this work, we present a novel Contextual Metric Learning (CML) method for learning a set of contextual distance metrics for real world multi-label images. The relationships between classes are formulated as contextual constraints for the optimization framework to leverage the learning performance. In the experiment, we apply the proposed method for automatic image annotation task. The experimental results show that our approach outperforms the start-of-the-art DML algorithms.