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Realtime Hierarchical Clustering Based on Boundary and Surface Statistics

  • Dominik Alexander KleinEmail author
  • Dirk Schulz
  • Armin Bernd Cremers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)

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.

Keywords

Gradient Magnitude Agglomerative Cluster Object Candidate Adjacency List Runtime Complexity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dominik Alexander Klein
    • 1
    Email author
  • Dirk Schulz
    • 1
  • Armin Bernd Cremers
    • 2
  1. 1.Department of Cognitive Mobile SystemsFraunhofer FKIEWachtbergGermany
  2. 2.Bonn-Aachen International Center for Information Technology (B-IT)BonnGermany

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