BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Automatic image annotation via local multi-label classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Multi-label learning by instance differentiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Context-based multi-label image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning instance-to-class distance for human action recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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
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In multi-label learning, an image containing multiple objects can be assigned to multiple labels, which makes it more challenging than traditional multi-class classification task where an image is assigned to only one label. In this paper, we propose a multi-label learning framework based on Image-to-Class (I2C) distance, which is recently shown useful for image classification. We adjust this I2C distance to cater for the multi-label problem by learning a weight attached to each local feature patch and formulating it into a large margin optimization problem. For each image, we constrain its weighted I2C distance to the relevant class to be much less than its distance to other irrelevant class, by the use of a margin in the optimization problem. Label ranks are generated under this learned I2C distance framework for a query image. Thereafter, we employ the label correlation information to split the label rank for predicting the label(s) of this query image. The proposed method is evaluated in the applications of scene classification and automatic image annotation using both the natural scene dataset and Microsoft Research Cambridge (MSRC) dataset. Experiment results show better performance of our method compared to previous multi-label learning algorithms.