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Abstract

Most current methods for multi-class object classification
and localization work as independent 1-vs-rest classifiers. They decide
whether and where an object is visible in an image purely on a per-class
basis. Joint learning of more than one object class would generally be
preferable, since this would allow the use of contextual information such
as co-occurrence between classes. However, this approach is usually not
employed because of its computational cost.
In this paper we propose a method to combine the efficiency of single
class localization with a subsequent decision process that works jointly
for all given object classes. By following a multiple kernel learning (MKL)
approach, we automatically obtain a sparse dependency graph of relevant
object classes on which to base the decision. Experiments on the
PASCAL VOC 2006 and 2007 datasets show that the subsequent joint
decision step clearly improves the accuracy compared to single class
detection.