Multi-Class Segmentation with Relative Location Prior |
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Authors: | Stephen Gould Jim Rodgers David Cohen Gal Elidan Daphne Koller |
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Affiliation: | (1) Department of Computer Science, Stanford University, Stanford, CA, USA |
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Abstract: | Multi-class image segmentation has made significant advances in recent years through the combination of local and global features.
One important type of global feature is that of inter-class spatial relationships. For example, identifying “tree” pixels
indicates that pixels above and to the sides are more likely to be “sky” whereas pixels below are more likely to be “grass.”
Incorporating such global information across the entire image and between all classes is a computational challenge as it is
image-dependent, and hence, cannot be precomputed.
In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as
a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which
are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine
this with appearance-based features to provide a final segmentation. We compare our results to recent published results on
several multi-class image segmentation databases and show that the incorporation of relative location information allows us
to significantly outperform the current state-of-the-art. |
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Keywords: | Multi-class image segmentation Segmentation Relative location |
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