Abstract
Shoeprints reflect some physiological characteristics of human beings, similar to fingerprints, which are important clues for criminal investigations. Extraction of shoeprints from images taken in crime scenes is a key preprocessing of the shoeprint image retrieval. It can be seen as a binary semantic image segmentation problem. Both traditional algorithms and existing deep learning approaches perform poorly for this problem, since shoeprint images contain various and strong background textures, are contaminated by serious noises and incomplete usually. This paper innovatively presents a framework with generative adversarial net (GAN) for the problem. Multiple generative networks, loses and adversarial networks are designed and compared within the framework. We also compared our method with the-state-of-the-art deep learning approaches on the professional shoeprint dataset with the evaluation criterion called MSS. The MSS of FCN, Deeplab-v3 and our method are 50.3%, 61.5%, and 75% on the dataset.
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
References
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–166 (2004)
Venkatesan, R.: Cluster Analysis for Segmentation. In: Technical Note M-0748/Published March 22, University of Virginia. Darden Business Publishing, Charlottesville, VA (2007)
Fan, J., Zeng, G., Body, M. Hacid, M.S.: Seeded region growing: an extensive and comparative study. Pattern Recogn. Lett. 26(8), 1139–1156 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
Chen, L.C., Papandreou, G., Schroff, F.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)
Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062 (2014)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2016)
Zhang, T., Qi, G.J., Xiao, B., Wang, J.: Interleaved group convolutions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4373–4382 (2017)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in neural information processing systems, pp. 2234–2242 (2016)
Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. Nature 521(7553), 436 (2015)
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240–248. Springer, Cham (2017)
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
Maas, A., Hannun, A., Ng, A.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML Workshop on Deep Learning for Audio, Speech and Language Processing (2013)
Radford, A., Metz, L., Chintala, S., et al.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11–19 (2017)
Acknowledgements
This work is supported by the Natural Science Foundation of China [grant numbers 61572099 and 61772104].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cao, J., Pan, R., Wang, L., Xu, X., Su, Z. (2019). Shoeprint Extraction via GAN. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-34879-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34878-6
Online ISBN: 978-3-030-34879-3
eBook Packages: Computer ScienceComputer Science (R0)