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Employing deep part-object relationships for salient object detection
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Liu, Yi, Zhang, Qiang, Zhang, Dingwen and Han, Jungong (2020) Employing deep part-object relationships for salient object detection. In: ICCV 2019. International conference on computer vision, Seoul, Korea, 27 Oct - 2 Nov 2019. Published in: 2019 IEEE/CVF International Conference on Computer Vision (ICCV) ISBN 9781728148045. ISSN 1550-5499. doi:10.1109/ICCV.2019.00132
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WRAP-Employing-deep-relationships-salient-object-detection-Han-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1592Kb) | Preview |
Official URL: https://doi.org/10.1109/ICCV.2019.00132
Abstract
Despite Convolutional Neural Networks (CNNs) based methods have been successful in detecting salient objects, their underlying mechanism that decides the salient intensity of each image part separately cannot avoid inconsistency of parts within the same salient object. This would ultimately result in an incomplete shape of the detected salient object. To solve this problem, we dig into part-object relationships and take the unprecedented attempt to employ these relationships endowed by the Capsule Network (CapsNet) for salient object detection. The entire salient object detection system is built directly on a Two-Stream Part-Object Assignment Network (TSPOANet) consisting of three algorithmic steps. In the first step, the learned deep feature maps of the input image are transformed to a group of primary capsules. In the second step, we feed the primary capsules into two identical streams, within each of which low-level capsules (parts) will be assigned to their familiar high-level capsules (object) via a locally connected routing. In the final step, the two streams are integrated in the form of a fully connected layer, where the relevant parts can be clustered together to form a complete salient object. Experimental results demonstrate the superiority of the proposed salient object detection network over the state-of-the-art methods.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||
| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Library of Congress Subject Headings (LCSH): | Neural Networks (Computer), Computer vision, Image processing -- Digital techniques, Neural networks (Computer science) -- Computer simulation | |||||||||
| Journal or Publication Title: | 2019 IEEE/CVF International Conference on Computer Vision (ICCV) | |||||||||
| Publisher: | IEEE | |||||||||
| ISBN: | 9781728148045 | |||||||||
| ISSN: | 1550-5499 | |||||||||
| Official Date: | 27 February 2020 | |||||||||
| Dates: |
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| Date of first compliant deposit: | 17 September 2019 | |||||||||
| DOI: | 10.1109/ICCV.2019.00132 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | ICCV 2019. International conference on computer vision | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | Seoul, Korea | |||||||||
| Date(s) of Event: | 27 Oct - 2 Nov 2019 | |||||||||
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