Style Transfer Via Image Component Analysis
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AbstractExample-based stylization provides an easy way of making artistic effects for images and videos. However, most existing methods do not consider the content and style separately. In this paper, we propose a style transfer algorithm via a novel component analysis approach, based on various image processing techniques. First, inspired by the steps of drawing a picture, an image is decomposed into three components: draft, paint and edge, which describe the content, main style, and strengthened strokes along the boundaries. Then the style is transferred from the template image to the source image in the paint and edge components. Style transfer is formulated as a global optimization problem by using Markov random fields, and a coarse-to-fine belief propagation algorithm is used to solve the optimization problem. To combine the draft component and the obtained style information, the final artistic result can be achieved via a reconstruction step. Compared to other algorithms, our method not only synthesizes the style, but also preserves the image content well. We also extend our algorithm from single image stylization to video personalization, by maintaining the temporal coherence and identifying faces in video sequences. The results indicate that our approach performs excellently in stylization and personalization for images and videos.
All Author(s) ListZhang W, Cao C, Chen SF, Liu JZ, Tang XO
Journal nameIEEE Transactions on Multimedia
Year2013
Month11
Day1
Volume Number15
Issue Number7
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1594 - 1601
ISSN1520-9210
eISSN1941-0077
LanguagesEnglish-United Kingdom
KeywordsExample-based stylization; non-photorealistic rendering; video stylization and personalization
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING; Telecommunications; TELECOMMUNICATIONS

Last updated on 2021-13-01 at 00:05