University of Oulu

Wei Ke, Jie Chen, Qixiang Ye, Deep contour and symmetry scored object proposal, Pattern Recognition Letters, Volume 119, 2019, Pages 172-179, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2018.01.004

Deep contour and symmetry scored object proposal

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Author: Ke, Wei1,2; Chen, Jie2; Ye, Qixiang1
Organizations: 1School of Electronic, Electrical and Communication Engineering, University of Chinese of Academy of Sciences, Beijing, 101408, China
2Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, 90570, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2019052316707
Language: English
Published: Elsevier, 2019
Publish Date: 2021-03-01
Description:

Abstract

Object proposal has been successfully applied in recent supervised and weakly supervised visual object detection tasks to improve the computational efficiency. The classical grouping-based object proposal approach can produce region proposals with high localization accuracy, but incorporates significant redundancy for the lack of object confidence to evaluate the proposals. In this paper, we propose leveraging the essential properties of images, i.e., contour and symmetry, to score the redundant region proposals. Specifically, the contour and symmetry are extracted by a Simultaneous Contour and Symmetry Detection Network (SCSDN) and used to score the bounding box with a Bayesian framework, which guarantees that the scoring procedure is adaptive to general objects. A subset of high-scored proposals reserves the recall rate, while can also significantly decrease the redundancy. Experimental results show that the proposed approach improves the baseline by increasing the recall rate from 0.87 to 0.89 on the PASCAL VOC 2007 dataset. It also outperforms the state-of-the-art on AUC and uses much fewer object proposals to achieve comparable recall rate.

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Series: Pattern recognition letters
ISSN: 0167-8655
ISSN-E: 1872-7344
ISSN-L: 0167-8655
Issue: 119
Pages: 172 - 179
DOI: 10.1016/j.patrec.2018.01.004
OADOI: https://oadoi.org/10.1016/j.patrec.2018.01.004
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
FCN
Funding: This work was supported in part by the National Science Foundation of China under Grant 61671427, and Beijing Municipal Science and Technology Commission under Grant Z161100001616005.
Copyright information: © 2018 Published by Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/