Name
Abstract | ||
|---|---|---|
We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We ask crowd workers to sketch particular photographic objects sampled from 125 categories and acquire 75,471 sketches of 12,500 objects. The Sketchy database gives us fine-grained associations between particular photos and sketches, and we use this to train cross-domain convolutional networks which embed sketches and photographs in a common feature space. We use our database as a benchmark for fine-grained retrieval and show that our learned representation significantly outperforms both hand-crafted features as well as deep features trained for sketch or photo classification. Beyond image retrieval, we believe the Sketchy database opens up new opportunities for sketch and image understanding and synthesis. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2897824.2925954 | ACM Trans. Graph. |
Keywords | Field | DocType |
Sketch-based image retrieval,Deep learning,Siamese network,Triplet network,Image synthesis | Computer vision,Feature vector,Computer graphics (images),Computer science,Image retrieval,Image synthesis,Artificial intelligence,Deep learning,Database,Sketch | Journal |
Volume | Issue | ISSN |
35 | 4 | 0730-0301 |
Citations | PageRank | References |
97 | 2.70 | 42 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patsorn Sangkloy | 1 | 143 | 5.60 |
| Nathan Burnell | 2 | 97 | 2.70 |
| Cusuh Ham | 3 | 104 | 3.80 |
| James Hays | 4 | 3942 | 172.72 |