Abstract
Content-Based Image Retrieval (CBIR) system enables us to access images using only images as queries, instead of keywords. Photorealistic images, and hand-drawn sketch image can be used as a queries as well. Recently, convolutional neural networks (CNNs) are used to discriminate images including sketches. However, the tasks are limited to classifying only one type of images, either photo or sketch images, due to the lack of a large dataset of sketch images and the large difference of their visual characteristics. In this paper, we introduce a simple way to prepare training datasets, which can enable the CNN model to classify both types of images by color transforming photo and illustration images. Through the training experiment, we show that the proposed method contributes to the improvement of classification accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Y., Zang, D., Lu, G., Ma, W.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40, 262–282 (2007)
Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: Sketch-based image retrieval: benchmark and bag-of-features descriptors. IEEE Trans. Visual Comput. Graphics 17, 1624–1636 (2011)
Cao, Y., Wang, H., Wang, C., Li, Z., Zhang, L., Zang, L.: MindFinder: interactive sketch-based image search. In: Proceedings of ACM Multimedia International Conference, pp. 1605–1608 (2010)
Cao, Y., Wang, C., Zhang, L., Zhang, L.: Edgel index for large-scale sketch-based image search. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 761–768 (2011)
Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Trans. Graph. 31, 1–10 (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1106–1114 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. arXiv:1512.03385 (2015)
Yang, Y., Hospedales, T.M.: Deep Neural Networks for Sketch Recognition. arXiv:1501.07873 (2015)
Yu, Q., Yang, Y., Song, Y., Xiang, T., Hospedales, T.: Sketch-a-Net that beats humans. In: Proceedings of the British Machine Vision Conference, pp. 7.1–7.12 (2015)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)
Bottou, L.: Stochastic gradient descent tricks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 421–436. Springer, Heidelberg (2012)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girchick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv:1408.5093 (2014)
Acknowledgments
The work has been supported by MEXT Grant-in-Aid for Scientific Research (A) 15H01710.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sasaki, K., Yamakawa, M., Sekiguchi, K., Ogata, T. (2016). Classification of Photo and Sketch Images Using Convolutional Neural Networks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-44781-0_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44780-3
Online ISBN: 978-3-319-44781-0
eBook Packages: Computer ScienceComputer Science (R0)