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A Conditional Random Field for Multiple-Instance Learning

T. Deselaers and V. Ferrari
ICML
June 2010

Abstract

We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. The parameters of MI-CRF are learned using constraint generation. Furthermore, we show that MI-CRF can incorporate previous MIL algorithms to improve on their results. MI-CRF obtains competitive results on five standard MIL datasets.


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@InProceedings{eth_biwi_00730,
  author = {T. Deselaers and V. Ferrari},
  title = {A Conditional Random Field for Multiple-Instance Learning},
  booktitle = {ICML},
  year = {2010},
  month = {June},
  keywords = {}
}