Multi-label learning by Image-to-Class distance for scene classification and image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Recognition of adult images, videos, and web page bags
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Ensemble multi-instance multi-label learning approach for video annotation task
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Graphical feature selection for multilabel classification tasks
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Information Sciences: an International Journal
Multi-instance methods for partially supervised image segmentation
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Multilabel classifiers with a probabilistic thresholding strategy
Pattern Recognition
Rank-loss support instance machines for MIML instance annotation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
MI2LS: multi-instance learning from multiple informationsources
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
M4L: Maximum margin Multi-instance Multi-cluster Learning for scene modeling
Pattern Recognition
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Constrained instance clustering in multi-instance multi-label learning
Pattern Recognition Letters
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Multi-instance multi-label learning (MIML) deals with the problem where each training example is associated with not only multiple instances but also multiple class labels. Previous MIML algorithms work by identifying its equivalence in degenerated versions of multi-instance multi-label learning. However, useful information encoded in training examples may get lost during the identification process. In this paper, a maximum margin method is proposed for MIML which directly exploits the connections between instances and labels. The learning task is formulated as a quadratic programming (QP) problem and implemented in its dual form. Applications to scene classification and text categorization show that the proposed approach achieves superior performance over existing MIML methods.