Abstract
The paper presents a deep Convolutional Neural Network (CNN) framework for free-hand sketch recognition. One of the main challenges in free-hand sketch recognition is to increase the recognition accuracy on sketches drawn by different people. To overcome this problem, we use deep Convolutional Neural Networks (CNNs) that have dominated top results in the field of image recognition. And we use the contours of natural images for training, because sketches drawn by different people may be very different and databases of the sketch images for training are very limited. We propose a CNN training on contours that performs well on sketch recognition over different databases of the sketch images. And we make some adjustments to the contours for training and reach higher recognition accuracy. Experimental results show the effectiveness of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yu, Q., Yang, Y., Song, Y.-Z., Xiang, T., Hospedales, T.: Sketch-a-net that beats humans. In: British Machine Vision Conference (BMVC) (2015)
Seddati, O., Dupont, S., Mahmoudi, S.: DeepSketch: deep convolutional neural networks for sketch recognition and similarity search. In: International Workshop on Content-Based Multimedia Indexing, pp. 1–6 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25(2), 2012 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Eprint Arxiv (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Trans. Graph. 31(4), 44 (2012)
Schneider, R.G., Tuytelaars, T.: Sketch classification and classification-driven analysis using fisher vectors. ACM Trans. Graph. 33(6), 174:1–174:9 (2014)
Cao, X., Zhang, H., Liu, S., Guo, X., Lin, L.: SYM-FISH: A Symmetry-Aware Flip Invariant Sketch Histogram Shape Descriptor. In: IEEE International Conference on Computer Vision, pp. 313–320. IEEE (2013)
Li, Y., Song, Y., Gong, S.: Sketch recognition by ensemble matching of structured features. In: British Machine Vision Conference (BMVC), pp. 1–11 (2013)
Li, Y., Hospedales, T.M., Song, Y.-Z., Gong, S.: Free-hand sketch recognition by multi-kernel feature learning. Comput. Vis. Image Underst. 31, 1–11 (2015)
Hammond, T., Davis, R.: Ladder, a sketching language for user interface developers. Comput. Graph. 29(4), 518–532 (2005)
Laviola, J.J., Zeleznik, R.C.: Mathpad 2: a system for the creation and exploration of mathematical sketches. ACM Trans. Graph. 23(3), 432–440 (2004)
Sarvadevabhatla, R.K., Babu, R.V.: Freehand sketch recognition using deep features. Computer Science (2015)
Zhang, Y., Qian, X., Tan, X.: Sketch-based image retrieval using contour segments. In: IEEE International Workshop on Multimedia Signal Processing (2015)
Qian, X., Tan, X., Zhang, Y., Hong, R., Wang, M.: Enhancing sketch-based image retrieval by re-ranking and relevance feedback. IEEE Trans. Image Process. 25(1), 195–208 (2015)
Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: Sketch-based image retrieval: benchmark and bag-of-features descriptors. IEEE Trans. Vis. Comput. Graph. 17(11), 1624–1636 (2010)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)
Wang, F., Kang, L., Li, Y.: Sketch-based 3D shape retrieval using convolutional neural networks. Eprint Arxiv (2015)
Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: Sketch-based 3D shape retrieval. ACM SIGGRAPH 31, 13–15 (2010). ACM
Pu, J., Lou, K., Ramani, K.: A 2D sketch-based user interface for 3D cad model retrieval. Comput. Aided Des. Appl. 2(6), 717–725 (2007)
Qian, X., Zhang, Y., Tan, X., Han, J.: Sketch-based image retrieval by salient contour reinforcement. IEEE Trans. Multimedia 18(8), 1 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, Y., Zhang, Y., Qian, X. (2016). Deep Neural Networks for Free-Hand Sketch Recognition. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_68
Download citation
DOI: https://doi.org/10.1007/978-3-319-48896-7_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48895-0
Online ISBN: 978-3-319-48896-7
eBook Packages: Computer ScienceComputer Science (R0)