How important are Deformable Parts in the Deformable Parts Model?
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by
Santosh K. Divvala and Alexei A. Efros and Martial Hebert
2012
Abstract
The main stated contribution of the Deformable Parts Model (DPM) detector of
Felzenszwalb et al. (over the Histogram-of-Oriented-Gradients approach of Dalal
and Triggs) is the use of deformable parts. A secondary contribution is the
latent discriminative learning. Tertiary is the use of multiple components. A
common belief in the vision community (including ours, before this study) is
that their ordering of contributions reflects the performance of detector in
practice. However, what we have experimentally found is that the ordering of
importance might actually be the reverse. First, we show that by increasing the
number of components, and switching the initialization step from their
aspect-ratio, left-right flipping heuristics to appearance-based clustering,
considerable improvement in performance is obtained. But more intriguingly, we
show that with these new components, the part deformations can now be
completely switched off, yet obtaining results that are almost on par with the
original DPM detector. Finally, we also show initial results for using multiple
components on a different problem -- scene classification, suggesting that this
idea might have wider applications in addition to object detection.
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