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Accelerating Deformable Part Models with Branch-and-Bound

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Perspectives in Shape Analysis

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

Deformable Part Models (DPMs) play a prominent role in current object recognition research, as they rigorously model the shape variability of an object category by breaking an object into parts and modelling the relative locations of the parts. Still, inference with such models requires solving a combinatorial optimization task. In this chapter, we will see how Branch-and-Bound can be used to efficiently perform inference with such models. Instead of evaluating the classifier score exhaustively for all part locations and scales, such techniques allow us to quickly focus on promising image locations. The core problem that we will address is how to compute bounds that accommodate part deformations; this allows us to apply Branch-and-Bound to our problem. When comparing to a baseline DPM implementation, we obtain exactly the same results but can perform the part combination substantially faster, yielding up to tenfold speedups for single object detection, or even higher speedups for multiple objects.

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Notes

  1. 1.

    In our comparisons we use the original- and faster-GDT algorithm of [11] instead of the one provided in [17].

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Acknowledgements

I thank the two anonymous reviewers and Stefan Kinauer for feedback that helped improve this manuscript. I am grateful to the authors of [7, 10, 22, 29, 30] for making their code available. This work was funded by grants ANR-10-JCJC-0205 and FP7-Reconfig.

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Authors and Affiliations

  1. Center for Visual Computing, Centrale-Supélec and INRIA-Saclay, Grande Voie des Vignes, 92295, Chatenay-Malabry, France

    Iasonas Kokkinos

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  1. Iasonas Kokkinos
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Correspondence to Iasonas Kokkinos .

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Editors and Affiliations

  1. Institute for Applied Mathematics and Scientific Computing, Brandenburg University of Technology, Cottbus, Germany

    Michael Breuß

  2. Israel Institute of Technology Technion, Haifa, Israel

    Alfred Bruckstein

  3. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Petros Maragos

  4. INRIA, Grenoble Rhône-Alpes, Saint Ismier, France

    Stefanie Wuhrer

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Kokkinos, I. (2016). Accelerating Deformable Part Models with Branch-and-Bound. In: Breuß, M., Bruckstein, A., Maragos, P., Wuhrer, S. (eds) Perspectives in Shape Analysis. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24726-7_12

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