On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Supervised versus multiple instance learning: an empirical comparison
ICML '05 Proceedings of the 22nd international conference on Machine learning
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
M3IC: maximum margin multiple instance clustering
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A globally optimal approach for 3D elastic motion estimation from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
MIForests: multiple-instance learning with randomized trees
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering
IEEE Transactions on Neural Networks
Semantic hashing using tags and topic modeling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Learning compact hashing codes for efficient tag completion and prediction
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Weighted hashing for fast large scale similarity search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Multiple Instance Learning (MIL) has been widely used in various applications including image classification. However, existing MIL methods do not explicitly address the multi-target problem where the distributions of positive instances are likely to be multi-modal. This strongly limits the performance of multiple instance learning in many real world applications. To address this problem, this paper proposes a novel discriminative data-dependent mixture-model method for multiple instance learning (MM-MIL) approach in image classification. The new method explicitly handles the multi-target problem by introducing a data-dependent mixture model, which allows positive instances to come from different clusters in a flexible manner. Furthermore, the kernelized representation of the proposed model allows effective and efficient learning in high dimensional feature space. An extensive set of experimental results demonstrate that the proposed new MM-MIL approach substantially outperforms several state-of-art MIL algorithms on benchmark datasets.