Pixelated Semantic Colorization

Jiaojiao Zhao, Jungong Han, Ling Shao, Cees Snoek

Research output: Contribution to journalArticlepeer-review

43 Citations (SciVal)
22 Downloads (Pure)

Abstract

While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale 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.
Original languageEnglish
Pages (from-to)818–834
Number of pages17
JournalInternational Journal of Computer Vision
Volume128
Issue number4
Early online date07 Dec 2019
DOIs
Publication statusPublished - 01 Apr 2020
Externally publishedYes

Keywords

  • Image colorization
  • Pixelated semantics
  • Semantic segmentation

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