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
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Instance Learning of Real-Valued Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Knowledge Engineering Review
Vocabulary-Based Approaches for Multiple-Instance Data: A Comparative Study
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
An EM-Approach for clustering multi-instance objects
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problem have been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e., leaving out other learning tasks such as regression). In order to perform our study, we implemented fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods.