%0 Conference Paper %1 Deselaers2010 %A Deselaers, T. %A Ferrari, V. %B ICML %D 2010 %K Learning classification ConditionalRandomField Optimization FeatureSelection %T A Conditional Random Field for Multiple-Instance Learning %U http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=730&mode=&lang=de %X 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.

@inproceedings{Deselaers2010, 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.}, added-at = {2010-12-09T16:35:40.000+0100}, author = {Deselaers, T. and Ferrari, V.}, biburl = {https://www.bibsonomy.org/bibtex/29195f1b68359b53cb2a5b2bdb6b4a381/ipi_jn}, booktitle = {ICML}, file = {CRF Learning:E\:\\Literatursammlung\\Proceedings\\Sonstige\\Deselaers (2010) - A Conditional Random Field For Multiple-Instance Learning.pdf:PDF}, interhash = {8068a2781c0fe609b038989082de4c4d}, intrahash = {9195f1b68359b53cb2a5b2bdb6b4a381}, keywords = {Learning classification ConditionalRandomField Optimization FeatureSelection}, month = {June}, timestamp = {2010-12-09T16:35:40.000+0100}, title = {A Conditional Random Field for Multiple-Instance Learning}, url = {http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=730&mode=&lang=de}, year = 2010 }