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The main stated contribution of the Deformable Parts Model (DPM) detector ofFelzenszwalb et al. (over the Histogram-of-Oriented-Gradients approach of Dalaland Triggs) is the use of deformable parts. A secondary contribution is thelatent discriminative learning. Tertiary is the use of multiple components. Acommon belief in the vision community (including ours, before this study) isthat their ordering of contributions reflects the performance of detector inpractice. However, what we have experimentally found is that the ordering ofimportance might actually be the reverse. First, we show that by increasing thenumber of components, and switching the initialization step from theiraspect-ratio, left-right flipping heuristics to appearance-based clustering,considerable improvement in performance is obtained. But more intriguingly, weshow that with these new components, the part deformations can now becompletely switched off, yet obtaining results that are almost on par with theoriginal DPM detector. Finally, we also show initial results for using multiplecomponents on a different problem -- scene classification, suggesting that thisidea might have wider applications in addition to object detection.

How important are Deformable Parts in the Deformable Parts Model?