Name
Abstract | ||
|---|---|---|
Face sketch synthesis with a photo is challenging due to that the psychological mechanism of sketch generation is difficult to be expressed precisely by rules. Current learning-based sketch synthesis methods concentrate on learning the rules by optimizing cost functions with low-level image features. In this paper, a new face sketch synthesis method is presented, which is inspired by recent advances in sparse signal representation and neuroscience that human brain probably perceives images using high-level features which are sparse. Sparse representations are desired in sketch synthesis due to that sparseness can adaptively selects the most relevant samples which give best representations of the input photo. We assume that the face photo patch and its corresponding sketch patch follow the same sparse representation. In the feature extraction, we select succinct high-level features by using the sparse coding technique, and in the sketch synthesis process each sketch patch is synthesized with respect to high-level features by solving an $l_1$-norm optimization. Experiments have been given on CUHK database to show that our method can resemble the true sketch fairly well. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.526 | ICPR |
Keywords | Field | DocType |
sketch synthesis,new face sketch synthesis,sketch patch,sketch generation,face sketch synthesis,sketch synthesis process,sparse representation,current learning-based sketch synthesis,corresponding sketch patch,high-level feature,sparse coding,face recognition,feature extraction,encoding,cost function,optimization,computer graphics,face,image features,dictionaries | Computer vision,Facial recognition system,Pattern recognition,Feature (computer vision),Neural coding,Computer science,Sparse approximation,Feature extraction,Sketch recognition,Artificial intelligence,Computer graphics,Sketch | Conference |
Citations | PageRank | References |
9 | 0.62 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Chang | 1 | 39 | 5.01 |
| Mingquan Zhou | 2 | 266 | 65.04 |
| Yanjun Han | 3 | 9 | 0.62 |
| Xiaoming Deng | 4 | 68 | 7.59 |