Contour Detection and Hierarchical Image Segmentation
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 user-specified annotations. Computation at multiple image resolutions
provides a means of coupling our system to recognition applications.
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Text Reference
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik. Contour detection and hierarchical image segmentation. IEEE PAMI, 2011.BibTeX Reference
@article{ArbelaezMFM_PAMI_2011,AUTHOR = "Arbelaez, Pablo and Maire, Michael and Fowlkes, Charless and Malik, Jitendra",
TITLE = "Contour Detection and Hierarchical Image Segmentation",
JOURNAL = "IEEE PAMI",
VOLUME = "33(5)",
YEAR = "2011",
TAG = "grouping"
}