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Zhi-Chao Song

dblp:197/3238 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visual content generation and editing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visual content generation and editing › style transfer
color transfer
0.312017
Sufficient Image Appearance Transfer Combining Color and Texture · IEEE Trans. Multim. 2017
Visual content generation and editing › appearance transfer
image appearance transfer
0.312017
Sufficient Image Appearance Transfer Combining Color and Texture · IEEE Trans. Multim. 2017
Visual content generation and editing › texture synthesis
texture transfer
0.112017
Sufficient Image Appearance Transfer Combining Color and Texture · IEEE Trans. Multim. 2017

Methods — techniques the papers use, named apart from their topics

feature point set expansion · 0.3feature detection and matching · 0.3
YearPublicationVenuePosition
2017 Sufficient Image Appearance Transfer Combining Color and Texture
abstract
Traditional color transfer methods can achieve satisfactory results for transferring the color style from a reference image to a source image, provided that the source image shares the similar color mood with the reference image. However, color transfer solutions are always sensitive to color category, which cannot generate natural results when the contents of the reference image and the source image are different, e.g., a lush tree in the reference image and a bare tree in the source image. In this situation, it is insufficient only through color transfer to transfer the appearance from the reference image to the source image only through color transfer, since other information such as texture should also be considered. To obtain sufficient appearance transfer results, we propose a new image appearance transfer method combining both color and texture features. Given a source image and a reference image, our method starts with feature detection and matching between the source image and the reference image. Then, we design a new method for expanding feature point sets to get texture transfer mark (TTM) and color transfer mark (CTM). TTM and CTM will guide texture transfer and color transfer, respectively. We demonstrate our appearance transfer algorithm between quantities of images and compare with results of existing methods. Experiment results show that given only a single reference image, our approach can produce more sufficient appearance transfer results than the state-of-the-art algorithms.
Zhi-Chao Song, Shi-guang Liu
IEEE Trans. Multim.1