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Abstract:
Recent research has shown that the use of contextual cues significantly improves performance in sliding window type localization systems. In this work, we propose a method
that incorporates both global and local context information through appropriately defined
kernel functions. In particular, we make use of a weighted combination of kernels defined
over local spatial regions, as well as a global context kernel. The relative importance of
the context contributions is learned automatically, and the resulting discriminant function
is of a form such that localization at test time can be solved efficiently using a branch
and bound optimization scheme. By specifying context directly with a kernel learning
approach, we achieve high localization accuracy with a simple and efficient representation.
This is in contrast to other systems that incorporate context for which expensive
inference needs to be done at test time. We show experimentally on the PASCAL VOC
datasets that the inclusion of context can significantly improve localization performance,
provided the relative contributions of context cues are learned appropriately.
