Multi-Class Image Labeling with Top-Down Segmentation and Generalized Robust P^N Potentials

Georgios Floros, Konstantinos Rematas, Bastian Leibe
British Machine Vision Conference (BMVC'11).

We propose a novel formulation for the scene labeling problem which is able to combine object detections with pixel-level information in a Conditional Random Field (CRF) framework. Since object detection and multi-class image labeling are mutually informative problems, pixel-wise segmentation can benefit from powerful object detectors and vice versa. The main contribution of the current work lies in the incorporation of topdown object segmentations as generalized robust P N potentials into the CRF formulation. These potentials present a principled manner to convey soft object segmentations into a unified energy minimization framework, enabling joint optimization and thus mutual benefit for both problems. As our results show, the proposed approach outperforms the state-of-the-art methods on the categories for which object detections are available. Quantitative and qualitative experiments show the effectiveness of the proposed method.

» Show BibTeX

author = {Georgios Floros and
Konstantinos Rematas and
Bastian Leibe},
title = {Multi-Class Image Labeling with Top-Down Segmentation and Generalized
Robust {\textdollar}P{\^{}}N{\textdollar} Potentials},
booktitle = {British Machine Vision Conference, {BMVC} 2011, Dundee, UK, August
29 - September 2, 2011. Proceedings},
pages = {1--11},
year = {2011},
crossref = {DBLP:conf/bmvc/2011},
url = {},
doi = {10.5244/C.25.79},
timestamp = {Wed, 24 Apr 2013 17:19:07 +0200},
biburl = {},
bibsource = {dblp computer science bibliography,}

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