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