Name
Title | ||
|---|---|---|
A sketch recognition method based on transfer deep learning with the fusion of multi-granular sketches |
Abstract | ||
|---|---|---|
Most of existing sketch recognition methods focus on the contour/shape of whole sketches. They ignore different granularities of sketches during sketching. Stroke sequences of sketches often demonstrate the change of various granularities. In the progress of sketching, a coarser-grained contour gradually changes to a finer-grained object. Different granularities of sketch imply different levels of semantic information and play different roles in sketch recognition. In this paper, a transfer-deep-learning-based sketch recognition method--“sketch-transfer-net” is proposed. Sketch-transfer-net designs a novel fine-tuning strategy to use different granular sketches to fine-tune different layers of neural network. The extensive comparative experiments show that the proposed sketch-transfer-net can capture descriptive information of various granular sketches and therefore improve the performance of sketch recognition. In addition, the novel fine-turning strategy could weaken the negative effect in transfer learning and enable CNNs to be well trained on small sketch datasets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-019-08216-6 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Sketch recognition, Deep learning, Transfer learning | Pattern recognition,Computer science,Transfer of learning,Semantic information,Sketch recognition,Artificial intelligence,Natural language processing,Deep learning,Artificial neural network,Sketch | Journal |
Volume | Issue | ISSN |
78 | 24 | 1380-7501 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Zhao | 1 | 3 | 3.42 |
| Yang Liu | 2 | 2 | 0.71 |
| Yijuan Lu | 3 | 732 | 46.24 |
| Benpeng Xu | 4 | 1 | 0.35 |