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
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
A needle in a haystack: local one-class optimization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Robust one-class clustering using hybrid global and local search
ICML '05 Proceedings of the 22nd international conference on Machine learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A rate-distortion one-class model and its applications to clustering
Proceedings of the 25th international conference on Machine learning
MILIS: Multiple Instance Learning with Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object association across PTZ cameras using logistic MIL
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust subspace discovery via relaxed rank minimization
Neural Computation
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Existing work in the field of Multiple Instance Learning (MIL) have only looked at the standard two-class problem assuming both positive and negative bags are available. In this work, we propose the first analysis of the one-class version of MIL problem where one is only provided input data in the form of positive bags. We also propose an SVM-based formulation to solve this problem setting. To make the approach computationally tractable we further develop a iterative heuristic algorithm using instance priors. We demonstrate the validity of our approach with synthetic data and compare it with the two-class approach. While previous work in target tracking using MIL have made certain run-time assumptions (such as motion) to address the problem, we generalize the approach and demonstrate the applicability of our work to this problem domain. We develop a scene prior modeling technique to obtain foreground-background priors to aid our one-class MIL algorithm and demonstrate its performance on standard tracking sequences.