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A Hybrid convolutional neural network for sketch recognition.
- Source :
-
Pattern Recognition Letters . Feb2020, Vol. 130, p73-82. 10p. - Publication Year :
- 2020
-
Abstract
- • We propose a novel Hybrid CNN architecture to address the problem of sketch recognition. • S-Net extracts shape features, which is invariant for sketch rotation and transformation. • Hybrid CNN achieves state-of-the-art on sketch classification and sketch-based image retrieval tasks. • The classification accuracy is 84.42% and 82.74% on TU-Berlin and SketchX datasets, respectively. • The MAP of SBIR is 57.4% and 28.74% on SketchX and Flickr15K-Large datasets, respectively. With the popularity of touch-screen devices, it is becoming increasingly important to understand users' free-hand sketches in computer vision and human-computer interaction. Most of existing sketch recognition methods employ the similar strategies used in image recognition, relying on appearance information represented by hand-crafted features or deep features from convolutional neural networks. We believe that sketch recognition can benefit from learning both appearance and shape representation. In this paper, we propose a novel architecture, named Hybrid CNN, which is composed of A-Net and S-Net. They describe appearance information and shape information, respectively. Hybrid CNN is then comprehensively evaluated in the sketch classification and retrieval tasks on different datasets, including TU-Berlin, Sketchy and Flickr15k. Experimental results demonstrate that the Hybrid CNN achieves competitive accuracy compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 130
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 141663881
- Full Text :
- https://doi.org/10.1016/j.patrec.2019.01.006