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Pixelated semantic colorization
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Zhao, Jiaojiao, Han, Jungong, Shao, Ling and Snoek, Cees G. M. (2020) Pixelated semantic colorization. International Journal of Computer Vision, 128 . 818-834 . doi:10.1007/s11263-019-01271-4 ISSN 0920-5691.
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Official URL: http://doi.org/10.1007/s11263-019-01271-4
Abstract
While many image colorization algorithms have recently shown the capability of producing plausible color versions from grayscale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit
pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors
based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from
which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model:
through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed network includes two
branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes
a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on
Pascal VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more
realistic and finer results compared to the colorization state-of-the-art.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) T Technology > TR Photography |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Library of Congress Subject Headings (LCSH): | Computer vision -- Research, Photographs -- Coloring, Image processing -- Digital techniques, Semantic computing | ||||||||
| Journal or Publication Title: | International Journal of Computer Vision | ||||||||
| Publisher: | Springer | ||||||||
| ISSN: | 0920-5691 | ||||||||
| Official Date: | April 2020 | ||||||||
| Dates: |
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| Volume: | 128 | ||||||||
| Page Range: | 818-834 | ||||||||
| DOI: | 10.1007/s11263-019-01271-4 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | This is a post-peer-review, pre-copyedit version of an article published in International Journal of Computer Vision. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11263-019-01271-4 | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 11 December 2019 | ||||||||
| Date of first compliant Open Access: | 13 December 2019 |
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