Integrated segmentation and recognition of hand-printed numerals
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A Note on Learning from Multiple-Instance Examples
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis
Neural Processing Letters
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICML '05 Proceedings of the 22nd international conference on Machine learning
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
An empirical evaluation of supervised learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Revisiting Multiple-Instance Learning Via Embedded Instance Selection
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Multiple-instance learning with structured bag models
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Multiple-instance learning with instance selection via dominant sets
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Multi-instance methods for partially supervised image segmentation
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Beyond trees: adopting MITI to learn rules and ensemble classifiers for multi-instance data
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Multiple-instance learning as a classifier combining problem
Pattern Recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Robust multiple-instance learning with superbags
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Robust object tracking using enhanced random ferns
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many applications such as object classification, detection and tracking. This paper presents a novel multiple-instance learning algorithmfor randomized trees called MIForests. Randomized trees are fast, inherently parallel and multi-class and are thus increasingly popular in computer vision. MIForest combine the advantages of these classifiers with the flexibility of multiple instance learning. In order to leverage the randomized trees for MIL, we define the hidden class labels inside target bags as random variables. These random variables are optimized by training random forests and using a fast iterative homotopy method for solving the non-convex optimization problem. Additionally, most previously proposed MIL approaches operate in batch or off-line mode and thus assume access to the entire training set. This limits their applicability in scenarios where the data arrives sequentially and in dynamic environments.We show that MIForests are not limited to off-line problems and present an on-line extension of our approach. In the experiments, we evaluate MIForests on standard visual MIL benchmark datasets where we achieve state-of-the-art results while being faster than previous approaches and being able to inherently solve multi-class problems. The on-line version of MIForests is evaluated on visual object tracking where we outperform the state-of-the-art method based on boosting.