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
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
Adaptive p-posterior mixture-model kernels for multiple instance learning
Proceedings of the 25th international conference on Machine learning
Taking the bite out of automated naming of characters in TV video
Image and Vision Computing
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For face recognition from video streams often cues such as transcripts, subtitles or on-screen text are available. This information could be very valuable for improving the recognition performance. However, frequently this data can not be associated directly with just one of the visible faces. To overcome this limitations and to exploit valuable information, we define the task as a multiple instance learning (MIL) problem. We formulate a robust loss function that describes our problem and incorporates ambiguous and unreliable information sources and optimize it using Gradient Boosting. A new definition of the posterior probability of a bag, based on the Lp-norm, improves the ability to deal with varying bag sizes over existing formulations. The benefits of the approach are demonstrated for face recognition in videos on a publicly available benchmark dataset. In fact, we show that exploring new information sources can drastically improve the classification results. Additionally, we show its competitive performance on standard machine learning datasets.