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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Realtime Hierarchical Clustering Based on Boundary and Surface Statistics

verfasst von : Dominik Alexander Klein, Dirk Schulz, Armin Bernd Cremers

Erschienen in: Computer Vision – ACCV 2016

Verlag: Springer International Publishing

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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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Nächstes Kapitel Weakly-Supervised Video Scene Co-parsing
Fußnoten
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.
 
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Metadaten
Titel
Realtime Hierarchical Clustering Based on Boundary and Surface Statistics
verfasst von
Dominik Alexander Klein
Dirk Schulz
Armin Bernd Cremers
Copyright-Jahr
2017
Buch
Computer Vision – ACCV 2016

Print ISBN: 978-3-319-54180-8

Electronic ISBN: 978-3-319-54181-5

Copyright-Jahr: 2017

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