Unsupervised Learning of Models for Recognition
We present a method to learn object class models from unlabeled and
unsegmented cluttered scenes for the purpose of visual object recognition. We
focus on a particular type of model where objects are represented as flexible constellations of rigid parts (features). The variability within a class is represented
by a joint probability density function (pdf) on the shape of the constellation and
the output of part detectors. In a first stage, the method automatically identifies
distinctive parts in the training set by applying a clustering algorithm to patterns
selected by an interest operator. It then learns the statistical shape model using
expectation maximization. The method achieves very good classification results
on human faces and rear views of cars.
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Text Reference
Markus Weber, Max Welling, and Pietro Perona. Unsupervised learning of models for recognition. In ECCV, I: 18–32. 2000.BibTeX Reference
@inproceedings{WeberWP_ECCV_2000,AUTHOR = "Weber, Markus and Welling, Max and Perona, Pietro",
TAG = "object_recognition",
TITLE = "Unsupervised Learning of Models for Recognition",
BOOKTITLE = "ECCV",
YEAR = "2000",
PAGES = "I: 18-32",
BIBSOURCE = "http://www.visionbib.com/bibliography/match595.html#TT42151"
}