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Situational Object Boundary Detection

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

Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4712-4721
Number of pages10
DOIs
Publication statusPublished - 2015

Abstract

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.

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