Contour Detection and Hierarchical Image Segmentation

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Contour Detection and Hierarchical Image Segmentation (2010)

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by Pablo Arbeláez , Michael Maire , Charless Fowlkes , Jitendra Malik

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	389 - 24 self

BibTeX

@MISC{Arbeláez10contourdetection,
    author = {Pablo Arbeláez and Michael Maire and Charless Fowlkes and Jitendra Malik},
    title = {Contour Detection and Hierarchical Image Segmentation},
    year = {2010}
}

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Abstract

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by userspecified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

Keyphrases

contour detection hierarchical image segmentation image segmentation spectral clustering contour detector hierarchical region tree computer vision multiple image resolution recognition application segmentation method present state-of-the-art algorithm fundamental problem userspecified annotation globalization framework generic machinery segmentation algorithm extensive experimental evaluation generated hierarchical segmentation

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