PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Localizing Objects while Learning Their Appearance
Thomas Deselaers, Bogdan Alexe and Vittorio Ferrari
In: ECCV 2010, 5-11 Sep 2010, Crete, Greece.

Abstract

Learning a new object class from cluttered training images is very challenging when the location of object instances is unknown. Previous works generally require objects covering a large portion of the images. We present a novel approach that can cope with extensive clutter as well as large scale and appearance variations between object instances. To make this possible we propose a conditional random field that starts from generic knowledge and then progressively adapts to the new class. Our approach simultaneously localizes object instances while learning an appearance model specific for the class. We demonstrate this on the challenging PASCAL VOC 2007 dataset. Furthermore, our method enables to train any state-of-the-art object detector in a weakly supervised fashion, although it would normally require object location annotations.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:6992
Deposited By:Thomas Deselaers
Deposited On:02 September 2010