Name
Abstract | ||
|---|---|---|
We present a novel image representation that characterizes a color image by an intensity image and a small number of color pixels. Our idea is based on solving an inverse problem of colorization: Given a color image, we seek to obtain an intensity image and a small subset of color pixels, which are called landmark pixels, so that the input color image can be recovered faithfully using the intensity image and the color cues provided by the selected landmark pixels. We develop an algorithm to derive the landmark-based sparse color representations from color images, and use the representations in the applications of color transfer and color correction. The computational cost for these applications is low owing to the sparsity of the proposed representation. The landmark-based representation is also preferable to statistics-based representations, e.g. color histograms and Gaussian mixture models, when we need to reconstruct the color image from a given representation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCV.2009.5459165 | ICCV |
Keywords | Field | DocType |
colorization inverse problem,image representation,statistics based representation,statistical analysis,landmark based sparse color representation,inverse problems,color correction,color transfer,intensity image,image colour analysis,color image,pixel,histograms,color histogram,gray scale,inverse problem,color,image reconstruction,gaussian mixture model,computational modeling | Computer vision,High color,Color space,Color histogram,Pattern recognition,Computer science,Color balance,Color correction,Artificial intelligence,RGB color model,False color,Color image | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1550-5499 E-ISBN : 978-1-4244-4419-9 |
ISBN | Citations | PageRank |
978-1-4244-4419-9 | 8 | 0.57 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tzu-Wei Huang | 1 | 17 | 1.72 |
| Hwann-Tzong Chen | 2 | 826 | 52.13 |