PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Conditional Random Field for Multiple-Instance Learning
Thomas Deselaers and Vittorio Ferrari
In: ICML 2010, 21-24 June 2010, Haifa, Israel.

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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6948
Deposited By:Thomas Deselaers
Deposited On:17 June 2010