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Abstract

Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location.
To perform localization, one can take a sliding window
approach, but this strongly increases the computational
cost, because the classifier function has to be evaluated over
a large set of candidate subwindows.
In this paper, we propose a simple yet powerful branchand-
bound scheme that allows efficient maximization of a
large class of classifier functions over all possible subimages.
It converges to a globally optimal solution typically
in sublinear time. We show how our method is applicable
to different object detection and retrieval scenarios. The
achieved speedup allows the use of classifiers for localization
that formerly were considered too slow for this task,
such as SVMs with a spatial pyramid kernel or nearest
neighbor classifiers based on the 2-distance. We demonstrate
state-of-the-art performance of the resulting systems
on the UIUC Cars dataset, the PASCAL VOC 2006 dataset
and in the PASCAL VOC 2007 competition.