Situational Object Boundary Detection

Jasper R. R. Uijlings, Vittorio Ferrari

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Intuitively, the appearance of true object boundaries varies from image to image. Hence the usual monolithic approach of training a single boundary predictor and applying it to all images regardless of their content is bound to be suboptimal. In this paper we therefore propose situational object boundary detection: We first define a variety of situations and train a specialized object boundary detector for each of them using. Then given a test image, we classify it into these situations using its context, which we model by global image appearance. We apply the corresponding situational object boundary detectors, and fuse them based on the classification probabilities. In experiments on ImageNet, Microsoft COCO, and Pascal VOC 2012 segmentation we show that our situational object boundary detection gives significant improvements over a monolithic approach. Additionally, our method substantially outperforms on semantic contour detection on their SBD dataset.
Original languageEnglish
Title of host publication2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)978-1-4673-6964-0
Publication statusPublished - 15 Oct 2015
Event2015 IEEE Conference on Computer Vision and Pattern Recognition - Boston, United States
Duration: 8 Jun 201510 Jun 2015

Publication series

ISSN (Print)1063-6919
ISSN (Electronic)1063-6919


Conference2015 IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2015
Country/TerritoryUnited States
Internet address


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