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Towards exaggerated image stereotypes

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Given a training set of images and a binary classifier,we introduce the notion of an exaggerated image stereotype forsome image class of interest, which emphasizes/exaggerates thecharacteristic patterns in an image and visualizes which visualinformation the classification relies on. This is useful for gaininginsight into the classification mechanism. The exaggerated imagestereotypes results in a proper trade-off between classificationaccuracy and likelihood of being generated from the class ofinterest. This is done by optimizing an objective function whichconsists of a discriminative term based on the classificationresult, and a generative term based on the assumption ofthe class distribution. We use this idea with Fisher’s LinearDiscriminant rule, and assume a multivariate normal distributionfor samples within a class. The proposed framework has beenapplied on handwritten digit data, illustrating specific featuresdifferentiating digits. Then it is applied to a face dataset usingActive Appearance Model (AAM), where male faces stereotypesare evolved from initial female faces.
TitelProceedings of The First Asian Conference on Pattern Recognition 2011,
Antal sider5
ISBN (Trykt)978-1-4577-0122-1
ISBN (Elektronisk)978-1-4577-0121-4
StatusUdgivet - 2011
BegivenhedAsian Conference on Pattern Recognition - Beijing, Kina
Varighed: 28 nov. 201128 nov. 2011
Konferencens nummer: 1


KonferenceAsian Conference on Pattern Recognition

ID: 34480902