Unsupervised Learning of Object Models

C. K. I. Williams, R. S. Zemel

Given a set of images, each of which contains one instance of a small but unknown set of objects imaged from a random viewpoint, we show how to perform unsupervised learning to discover the object classes. To group the data into objects we use a mixture model which is trained with the EM algorithm. We have investigated characterizing the the probability distribution for the features of each object either in terms of an object model or by a Gaussian distribution. We compare the performance of these two approaches on a dataset containing six different stick-animals, and on a dataset consisting of seven hand gestures.


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