Unsupervised Learning of Models for Recognition
Markus Weber, Max Welling, Pietro Perona
icon We present a method to learn object class models for the purpose of object recognition. We focus on a particular type of model where objects are represented as constellations of rigid features (parts). The variability within a class is represented by a joint probability density function (pdf) on the shape of the constellation and the output of the feature detectors. The pdf may be estimated from training data once a model structure (type and number of features) has been specified. The method automatically identifies distinctive features in the training set and learns the statistical shape model. It is assmed that a set of generic feature detectors is available for the learning algorithm to choose from. The entire set of model parameters is learned using expectation maximization.

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Text Reference

Markus Weber, Max Welling, and Pietro Perona. Unsupervised learning of models for recognition. In JNSC. 1999.

BibTeX Reference

    AUTHOR = "Weber, Markus and Welling, Max and Perona, Pietro",
    TAG = "object_recognition",
    TITLE = "Unsupervised Learning of Models for Recognition",
    YEAR = "1999",
    BIBSOURCE = "http://www.visionbib.com/bibliography/match595.html#TT42151"