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
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
A Sparse Support Vector Machine Approach to Region-Based Image Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On L_1-Norm Multi-class Support Vector Machines
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Learning discriminative localization from weakly labeled data
Pattern Recognition
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Automatic image categorization is a challenging computer vision problem, to which Multiple-instance Learning (MIL) has emerged as a promising approach. Typical current MIL schemes rely on binary one-versus-all classification, even for inherently multi-class problems. There are a few drawbacks with binary MIL when applied to a multi-class classification problem. This paper describes Multi-class Multiple-Instance Learning (McMIL). to image categorization that bypasses the necessity of constructing a series of binary classifiers. We analyze McMIL in depth to show why it is advantageous over binary MIL when strong target concept overlaps exist among the classes. We systematically valuate McMIL using two challenging image databases, and compare it with state-of-the-art binary MIL approaches. The McMIL achieves competitive classification accuracy, robustness to labeling noise, and effectiveness in capturing the target concepts using smaller amount of training data. We show that the learned target concepts from McMIL conform to human interpretation of the images.