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Ruru Jiang

dblp:169/3366 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1

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 · 44% Computational photography and imaging · 44% Multimedia analysis and retrieval · 13%

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

TopicWeightPapersLastEvidence papers
Computational photography and imaging › color imaging
exemplar-based color transfer
0.212015
Color Photo Makeover via Crowd Sourcing and Recoloring · ACM Multimedia 2015
Visual content generation and editing
image recoloring
0.212015
Color Photo Makeover via Crowd Sourcing and Recoloring · ACM Multimedia 2015
Multimedia analysis and retrieval
image retrieval
0.112015
Color Photo Makeover via Crowd Sourcing and Recoloring · ACM Multimedia 2015

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

superpixel segmentation · 0.2soft matching · 0.2crowdsourcing · 0.2
YearPublicationVenuePosition
2015 Color Photo Makeover via Crowd Sourcing and Recoloring
abstract
It is not always easy for amateur photographers to capture photos with desired colors even on a classic hot spot as the appearance of color photo dependent on many factors. This paper proposes a novel approach to recolor given photos via a crowdsourcing based makeover scheme. When a user input a photo to be recolored, the proposed system will first conduct favorite exemplars suggestion from the images hosted by the social media sites, by jointly leveraging contextual and visual information associated with the images. The recommended exemplars shall reveal the scene and context dependent color compositions and provide users with diverse possible color styles. Then, a novel superpixel-based recoloring scheme, incorporating color statistics, texture characteristics and spatial constraints into soft matching, is applied to generate new photos of desired color. Experiments and a user study demonstrate that the proposed color photo makeover is able to achieve robust recoloring results for various outdoor photos.
Wengang Cheng, Ruru Jiang, Chang Wen Chen
ACM Multimedia2