Data-Driven Image Color Theme Enhancement

被引:116
|
作者
Wang, Baoyuan [1 ]
Yu, Yizhou [1 ,2 ]
Wong, Tien-Tsin [3 ]
Chen, Chun [1 ]
Xu, Ying-Qing
机构
[1] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China
[2] Univ Illinois, Urbana, IL 61801 USA
[3] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
来源
ACM TRANSACTIONS ON GRAPHICS | 2010年 / 29卷 / 06期
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
Color Theme; Color Optimization; Histograms; Soft Segmentation; Texture Classes; PREFERENCE; APPEARANCE; EMOTIONS;
D O I
10.1145/1866158.1866172
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
It is often important for designers and photographers to convey or enhance desired color themes in their work. A color theme is typically defined as a template of colors and an associated verbal description. This paper presents a data-driven method for enhancing a desired color theme in an image. We formulate our goal as a unified optimization that simultaneously considers a desired color theme, texture-color relationships as well as automatic or user-specified color constraints. Quantifying the difference between an image and a color theme is made possible by color mood spaces and a generalization of an additivity relationship for two-color combinations. We incorporate prior knowledge, such as texture-color relationships, extracted from a database of photographs to maintain a natural look of the edited images. Experiments and a user study have confirmed the effectiveness of our method.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Data-Driven Convolutional Model for Digital Color Image Demosaicing
    de Gioia, Francesco
    Fanucci, Luca
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (21):
  • [2] A DATA-DRIVEN COLOR FEATURE LEARNING SCHEME FOR IMAGE RETRIEVAL
    Varior, Rahul Rama
    Wang, Gang
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 1334 - 1338
  • [3] Data-Driven Color Manifolds
    Nguyen, Chuong H.
    Ritschel, Tobias
    Seidel, Hans-Peter
    [J]. ACM TRANSACTIONS ON GRAPHICS, 2015, 34 (02):
  • [4] Theme 3: Trust in Data-Driven Research
    Rauber, Andreas
    Oyama, Satoshi
    Kashima, Hisashi
    Yanai, Naoto
    Li, Jiyi
    Takeuchi, Koh
    Aizawa, Akiko
    Plexousakis, Dimitris
    Flicker, Katharina
    [J]. ERCIM NEWS, 2024, (136): : 9 - 10
  • [5] Data-driven Enhancement of SVBRDF Reflectance Data
    Steinhausen, Heinz Christian
    den Brok, Dennis
    Merzbach, Sebastian
    Weinmann, Michael
    Klein, Reinhard
    [J]. PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 1: GRAPP, 2018, : 273 - 280
  • [6] Data-driven enhancement of facial attractiveness
    Leyvand, Tommer
    Cohen-Or, Daniel
    Dror, Gideon
    Lischinski, Dani
    [J]. ACM TRANSACTIONS ON GRAPHICS, 2008, 27 (03):
  • [7] Image Resolution Enhancement via Data-Driven Parametric Models in the Wavelet Space
    Xin Li
    [J]. EURASIP Journal on Image and Video Processing, 2007
  • [8] Image Resolution Enhancement via Data-Driven Parametric Models in the Wavelet Space
    Li, Xin
    [J]. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 2007, 2007 (1)
  • [9] Data-driven Multispectral Image Registration
    Yasir, Rahat
    Eramian, Mark
    Stavness, Ian
    Shirtliffe, Steve
    Duddu, Hema
    [J]. 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV), 2018, : 230 - 237
  • [10] DCST: A Data-driven Color/Spatial Transform-based Image Coding Method
    Wang, Yifan
    Mei, Zhanxuan
    Katsavounidis, Ioannis
    Kuo, C-C Jay
    [J]. APPLICATIONS OF DIGITAL IMAGE PROCESSING XLIV, 2021, 11842