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Realtime Hierarchical Clustering Based on Boundary and Surface Statistics

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10111))

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Abstract

Visual grouping is a key mechanism in human scene perception. There, it belongs to the subconscious, early processing and is key prerequisite for other high level tasks such as recognition. In this paper, we introduce an efficient, realtime capable algorithm which likewise agglomerates a valuable hierarchical clustering of a scene, while using purely local appearance statistics.

To speed up the processing, first we subdivide the image into meaningful, atomic segments using a fast Watershed transform. Starting from there, our rapid, agglomerative clustering algorithm prunes and maintains the connectivity graph between clusters to contain only such pairs, which directly touch in the image domain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. The core of this approach is our novel cluster distance: it combines boundary and surface statistics both in terms of appearance as well as spatial linkage. This yields state-of-the-art performance, as we demonstrate in conclusive experiments conducted on BSDS500 and Pascal-Context datasets.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Nearest-neighbor_chain_algorithm.

  2. 2.

    This is similar to the well known earth mover’s distance (EMD) on histograms, which is in fact the discretized \(\mathcal {W}_1\) distance.

  3. 3.

    http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/.

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Author information

Authors and Affiliations

  1. Department of Cognitive Mobile Systems, Fraunhofer FKIE, Wachtberg, Germany

    Dominik Alexander Klein & Dirk Schulz

  2. Bonn-Aachen International Center for Information Technology (B-IT), Bonn, Germany

    Armin Bernd Cremers

Authors
  1. Dominik Alexander Klein
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  2. Dirk Schulz
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  3. Armin Bernd Cremers
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Corresponding author

Correspondence to Dominik Alexander Klein .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Klein, D.A., Schulz, D., Cremers, A.B. (2017). Realtime Hierarchical Clustering Based on Boundary and Surface Statistics. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-54181-5_1

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