PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video
Christopher Williams
In: Sheffield Machine Learning Workshop, 7-10 Septmber 2004, Sheffield, England.


Williams and Titsias (2004) have shown how to carry out unsupervised greedy learning of multiple objects from images (GLOMO), building on the work of Jojic and Frey (2001). In this paper we show that the earlier work on GLOMO can be greatly speeded up for video sequence data by carrying out approximate tracking of the multiple objects in the scene. Our method is applied to raw image sequence data and extracts the objects one at a time. First, the moving background is learned, and moving objects are found at later stages. The algorithm recursively updates an appearance model of the tracked object so that possible occlusion of the object is taken into account which makes tracking stable. Joint work with Michalis Titsias

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EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:593
Deposited By:Christopher Williams
Deposited On:27 December 2004