SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning

David Weiss, Ben Taskar; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2035-2042

Abstract


We propose SCALPEL, a flexible method for object segmentation that integrates rich region-merging cues with midand high-level information about object layout, class, and scale into the segmentation process. Unlike competing approaches, SCALPEL uses a cascade of bottom-up segmentation models that is capable of learning to ignore boundaries early on, yet use them as a stopping criterion once the object has been mostly segmented. Furthermore, we show how such cascades can be learned efficiently. When paired with a novel method that generates better localized shape priors than our competitors, our method leads to a concise, accurate set of segmentation proposals; these proposals are more accurate on the PASCAL VOC2010 dataset than state-of-the-art methods that use re-ranking to filter much larger bags of proposals. The code for our algorithm is available online.

Related Material


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[bibtex]
@InProceedings{Weiss_2013_CVPR,
author = {Weiss, David and Taskar, Ben},
title = {SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}