Name
Title | ||
|---|---|---|
Distinguishing computer graphics from photographic images using local binary patterns |
Abstract | ||
|---|---|---|
With the ongoing development of rendering technology, computer graphics (CG) are sometimes so photorealistic that to distinguish them from photographic images (PG) by human eyes has become difficult. To this end, many methods have been developed for automatic CG and PG classification. In this paper, we explore the statistical difference of uniform gray-scale invariant local binary patterns (LBP) to distinguish CG from PG with the help of support vector machines (SVM). We select YCbCr as the color model. The original JPEG coefficients of Y and Cr components, and their prediction errors are used for LBP calculation. From each 2-D array, we obtain 59 LBP features. In total, four groups of 59 features are obtained from each image. The proposed features have been tested with thousands of CG and PG. Classification accuracy reaches 98.3% with SVM and outperforms the state-of-the-art works. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-40099-5_19 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
photographic image,pg classification,distinguishing computer graphics,local binary pattern,cr component,classification accuracy,lbp calculation,automatic cg,lbp feature,color model,2-d array,gray-scale invariant local binary,computer graphics | Computer vision,YCbCr,Computer science,Local binary patterns,Support vector machine,JPEG,Artificial intelligence,Color model,Invariant (mathematics),Rendering (computer graphics),Computer graphics | Conference |
Volume | Issue | ISSN |
7809 LNCS | null | 16113349 |
Citations | PageRank | References |
11 | 0.68 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaohong Li | 1 | 43 | 6.17 |
| Jingyu Ye | 2 | 90 | 4.70 |
| Yun Qing Shi | 3 | 518 | 23.34 |