Abstract
The traditional convolutional neural networks are heavy with millions of parameters and the classification accuracy is not high. To address this issue, we propose a novel model called parallel concatenated convolutional neural network with cross-layer connections. The model mainly includes parallel processing and concatenate operation. In parallel processing, the diversity of features is increased by using different sizes of convolution kernels. The parallel outputs are integrated together by concatenate operation. Meanwhile, an improved cross-layer connection structure is also added to the model. At the experimental stage, the model was tested on the Caltech-256 and Food-101 datasets, the experiment results indicate that the constructed PCNet (without cross-layer connections) increases the recognition accuracy by 2.54% and 7.31% compared to AlexNet, and the proposed RPCNet (with cross-layer connections) is improved by 6.12% and 12.28% compared to AlexNet.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3), 197–387 (2014)
Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 – Mining Discriminative Components with Random Forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 446–461. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_29
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proce. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint (2013) arXiv:1312.4400
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint (2014). arXiv:1409.1556
Huang, G., Liu, Z., Maaten, L.V.D., et al.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269. IEEE (2017)
Howard, A.G., Zhu, M., Chen, B., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint (2014). arXiv:1704.04861
Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 215–223 (2011)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint (2015). arXiv:1502.03167
Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: 31st AAAI Conference on Artificial Intelligence, pp. 4278–4284. AAAI Press, San Francisco (2017)
Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 675–678. ACM (2014)
Attokaren, D.J., Fernandes, I.G., Sriram, A., et al.: Food classification from images using convolutional neural networks. In: TENCON 2017 IEEE Region 10 Conference, pp. 2801–2806. IEEE (2017)
Yanai, K., Kawano, Y.: Food image recognition using deep convolutional network with pre-training and fine-tuning. In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6. IEEE (2015)
Pandey, P., Deepthi, A., Mandal, B., et al.: FoodNet: recognizing foods using ensemble of deep networks. IEEE Signal Process. Lett. 24(12), 1758–1762 (2017)
Acknowledgments
Authors acknowledge support of the National Natural Science Foundation of China (Grant Nos. 11465004). Authors are also thankful to the anonymous reviewers whose constructive suggestions helped improve and clarify this manuscript.
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
Li, P., Jiang, P., Zeng, S., Fan, R. (2019). Parallel Concatenated Network with Cross-layer Connections for Image Recognition. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-17642-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17641-9
Online ISBN: 978-3-030-17642-6
eBook Packages: Computer ScienceComputer Science (R0)