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SEEDS: Superpixels Extracted Via Energy-Driven Sampling

Authors
  • Van den Bergh, Michael1
  • Boix, Xavier1
  • Roig, Gemma1
  • Van Gool, Luc1
  • 1 ETH Zurich - Computer Vision Laboratory, Sternwartstrasse 7, Zurich, 8092, Switzerland , Zurich (Switzerland)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Jul 19, 2014
Volume
111
Issue
3
Pages
298–314
Identifiers
DOI: 10.1007/s11263-014-0744-2
Source
Springer Nature
Keywords
License
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Abstract

Superpixel algorithms aim to over-segment the image by grouping pixels that belong to the same object. Many state-of-the-art superpixel algorithms rely on minimizing objective functions to enforce color homogeneity. The optimization is accomplished by sophisticated methods that progressively build the superpixels, typically by adding cuts or growing superpixels. As a result, they are computationally too expensive for real-time applications. We introduce a new approach based on a simple hill-climbing optimization. Starting from an initial superpixel partitioning, it continuously refines the superpixels by modifying the boundaries. We define a robust and fast to evaluate energy function, based on enforcing color similarity between the boundaries and the superpixel color histogram. In a series of experiments, we show that we achieve an excellent compromise between accuracy and efficiency. We are able to achieve a performance comparable to the state-of-the-art, but in real-time on a single Intel i7 CPU at 2.8 GHz.

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