12. B. Leibe, K. Mikolajcyk, B. Schiele: Segmentation-Based Multi-Cue Integration for Object Detection
This paper proposes a novel method for integrating multiple
local
cues, i.e. local region detectors as well as descriptors, in the
context of object detection. Rather than to fuse the outputs of
several distinct classifiers in a fixed setup, our approach implements
a highly flexible combination scheme, where the contributions of all
individual cues are flexibly recombined depending on their explanatory power
for each new test image. The key idea behind our approach is to
integrate the cues over an estimated top-down segmentation, which
allows to quantify how much each of them contributed to the object
hypothesis. By combining those contributions on a per-pixel level, our
approach ensures that each cue only contributes to object regions for
which it is confident and that potential correlations between cues are
effectively factored out. Experimental results on several benchmark
data sets show that the proposed multi-cue combination scheme
significantly increases detection performance compared to any of its
constituent cues alone. Moreover, it provides an interesting
evaluation tool to analyze the complementarity of local feature
detectors and descriptors.