A Sketch Recognition Method Based on Deep Convolutional-Recurrent Neural Network

被引：3

作者：

Zhao P. ^{[1
,2
]}

Liu Y. ^{[1
,2
]}

Liu H. ^{[1
,2
]}

Yao S. ^{[1
,2
]}

机构：

[1] Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei

[2] School of Computer Science and Technology, Anhui University, Hefei

来源：

| 2018年 / Institute of Computing Technology卷 / 30期

关键词：

Deep convolutional neural network; Deep learning; Recurrent neural network; Sketch recognition; Stroke order information;

D O I：

10.3724/SP.J.1089.2018.16275

中图分类号：

学科分类号：

摘要：

The existing sketch recognition methods ignore the stroke order information in extracting the feature of the sketch. This paper took the advantage of the stroke order information of the sketch and proposed a sketch recognition method based on deep convolutional-recurrent neural network, which combined the deep convolutional neural network and recurrent neural network. Firstly, the proposed method extracted the strokes of the sketch in sequence and obtained an ordered sequence of subsketches with increasing number of strokes. Secondly, a deep convolutional neural network was adapted to extract the feature of each subsketch in the ordered subsketch sequence and an ordered feature sequence was generated. Finally, the ordered feature sequence was input into a modified recurrent neural network, which constructed the temporal relations among the different subsketches of the same sketch to improve the accuracy of the sketch recognition. The experimental results on the largest freehand sketch dataset which is the TU-Berlin Sketch dataset show that the proposed method can effectively improve the recognition accuracy of freehand sketches. © 2018, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：217 / 224

页数：7

共 23 条

[1]

Eitz M., Hays J., Alexa M., How do humans sketch objects?, ACM Transactions on Graphics, 31, 4, (2012)

[2]

Schneider R.G., Tuytelaars T., Sketch classification and classifi-cation-driven analysis using Fisher vectors, ACM Transactions on Graphics, 33, 6, (2014)

[3]

Eitz M., Hildebrand K., Boubekeur T., Et al., Sketch-based image retrieval: Benchmark and bag-of-features descriptors, IEEE Transactions on Visualization and Computer Graphics, 17, 11, pp. 1624-1636, (2011)

[4]

Wang F., Kang L., Li Y., Sketch-based 3D shape retrieval using convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1875-1883, (2015)

[5]

Klare B., Li Z.F., Jain A.K., Matching forensic sketches to mug shot photos, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 3, pp. 639-646, (2011)

[6]

Yu Q., Yang Y.X., Song Y.Z., Et al., Sketch-a-net that beats humans

[7]

Xiang Z., Tan H., Ma Z., Performance comparison of improved HOG, Gabor and LBP, Journal of Computer-Aided Design & Computer Graphics, 24, 6, pp. 787-792, (2012)

[8]

Lowe D.G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)

[9]

Carneiro G., Jepson A.D., Pruning local feature correspondences using shape context, Proceedings of the 17th International Conference on Pattern Recognition, 3, pp. 16-19, (2004)

[10]

Li Y., Hospedales T.M., Song Y.Z., Et al., Free-hand sketch recognition by multi-kernel feature learning, Computer Vision and Image Understanding, 137, pp. 1-11, (2015)

← 1 2 3 →