Multiple instance metric learning from automatically labeled bags of faces

Matthieu Guillaumin 1 Jakob Verbeek 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Metric learning aims at finding a distance that approximates a task-specific notion of semantic similarity. Typically, a Mahalanobis distance is learned from pairs of data labeled as being semantically similar or not. In this paper, we learn such metrics in a weakly supervised setting where "bags" of instances are labeled with "bags" of labels. We formulate the problem as a multiple instance learning (MIL) problem over pairs of bags. If two bags share at least one label, we label the pair positive, and negative otherwise. We propose to learn a metric using those labeled pairs of bags, leading to MildML, for multiple instance logistic discriminant metric learning. MildML iterates between updates of the metric and selection of putative positive pairs of examples from positive pairs of bags. To evaluate our approach, we introduce a large and challenging data set, Labeled Yahoo! News, which we have manually annotated and contains 31147 detected faces of 5873 different people in 20071 images. We group the faces detected in an image into a bag, and group the names detected in the caption into a corresponding set of labels. When the labels come from manual annotation, we find that MildML using the bag-level annotation performs as well as fully supervised metric learning using instance-level annotation. We also consider performance in the case of automatically extracted labels for the bags, where some of the bag labels do not correspond to any example in the bag. In this case MildML works substantially better than relying on noisy instance-level annotations derived from the bag-level annotation by resolving face-name associations in images with their captions.
Type de document :
Communication dans un congrès
Kostas Daniilidis and Petros Maragos and Nikos Paragios. ECCV 2010 - European Conference on Computer Vision, Sep 2010, Heraklion, Greece. Springer-Verlag, 6311, pp.634-647, 2010, Lecture Notes in Computer Science. <http://springerlink.metapress.com/content/811413113002347n/>. <10.1007/978-3-642-15549-9_46>
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Soumis le : lundi 20 décembre 2010 - 10:23:21
Dernière modification le : mercredi 9 juillet 2014 - 16:41:50
Document(s) archivé(s) le : lundi 5 novembre 2012 - 14:36:57

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Matthieu Guillaumin, Jakob Verbeek, Cordelia Schmid. Multiple instance metric learning from automatically labeled bags of faces. Kostas Daniilidis and Petros Maragos and Nikos Paragios. ECCV 2010 - European Conference on Computer Vision, Sep 2010, Heraklion, Greece. Springer-Verlag, 6311, pp.634-647, 2010, Lecture Notes in Computer Science. <http://springerlink.metapress.com/content/811413113002347n/>. <10.1007/978-3-642-15549-9_46>. <inria-00548639>

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