NDLI: Scene shape priors for superpixel segmentation

Content Provider	IEEE Xplore Digital Library
Author	Moore, A.P. Prince, S.J.D. Warrell, J. Mohammed, U. Jones, G.
Copyright Year	2009
Description	Author affiliation: Sharp Laboratories Europe, Oxford, 4 4GB, UK (Jones, G.) \|\| University College London, Gower Street, WC1E 6BT, UK (Moore, A.P.; Prince, S.J.D.; Warrell, J.; Mohammed, U.)
Abstract	Unsupervised over-segmentation of an image into super-pixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. In this paper we investigate incorporating a priori information into superpixel segmentations. We learn a probabilistic model that describes the spatial density of the object boundaries in the image. We then describe an over-segmentation algorithm that partitions this density roughly equally between superpixels whilst still attempting to capture local object boundaries. We demonstrate this approach using road scenes where objects in the center of the image tend to be more distant and smaller than those at the edge. We show that our algorithm successfully learns this foveated spatial distribution and can exploit this knowledge to improve the segmentation. Lastly, we introduce a new metric for evaluating vision labeling problems. We measure performance on a challenging real-world dataset and illustrate the limitations of conventional evaluation metrics.
Starting Page	771
Ending Page	778
File Size	1383801
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459246
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Shape Image segmentation Object detection Partitioning algorithms Laboratories Europe Educational institutions Roads Labeling
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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