Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks
We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential function...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 39(2017), 9 vom: 15. Sept., Seite 1839-1852 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2017
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential functions. Consequently, it can be used to encode different cardinality-based multi-instance assumptions, ranging from the standard MIL assumption to more general assumptions. In addition, this framework can be efficiently used for both binary and multiclass classification. To this end, an efficient inference algorithm and a discriminative latent max-margin learning algorithm are introduced to train and test the proposed multi-instance Markov network models. We evaluate the performance of the proposed framework on binary and multi-class MIL benchmark datasets as well as two challenging computer vision tasks: cyclist helmet recognition and human group activity recognition. Experimental results verify that encoding the degree of ambiguity in data can improve classification performance |
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| Beschreibung: | Date Completed 15.11.2018 Date Revised 15.11.2018 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2016.2613865 |