Abstract
Recent deep learning-based approaches have achieved remarkable performance in the animal-image classification field. However, previous deep learning-based approaches consume large amounts of computational resources, thus is not suitable for deployment under resource-constrained environments. To address this problem, we propose a novel Multiprocess Convolutional Network (MPNet). Specifically, this network contains two subnetworks. The first one employs a convolutional network to extract abstract semantic features from a horizontal viewpoint. To make full use of semantic information, we design the other subnetwork to extract features from a vertical viewpoint. Then we calculate the gram matrix of these feature maps by element-wise multiplication. Meanwhile, we adopt weight sharing strategy to reduce model parameters. Experiments on the Animals with Attributes (AWA) dataset has demonstrated that our proposed approach achieves 87.54% top-1 accuracy with 33.57MB parameters. Compared with the other state-of-the-art approaches, our model saves more computation cost and yields higher accuracy.
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
Ahmad, A., Dey, L.: A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl. Eng. 63(2), 503–527 (2007)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Branson, S., Van Horn, G., Belongie, S., Perona, P.: Bird species categorization using pose normalized deep convolutional nets. arXiv preprint arXiv:1406.2952 (2014)
Cao, Z., Principe, J.C., Ouyang, B., Dalgleish, F., Vuorenkoski, A.: Marine animal classification using combined CNN and hand-designed image features. In: OCEANS 2015-MTS/IEEE Washington, pp. 1–6. IEEE (2015)
Chen, G., Han, T.X., He, Z., Kays, R., Forrester, T.: Deep convolutional neural network based species recognition for wild animal monitoring. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 858–862. IEEE (2014)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, pp. 1–2. Prague (2004)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2012)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
He, K., Sun, J.: Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5353–5360 (2015)
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)
Howard, A.G., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Kong, S., Fowlkes, C.: Low-rank bilinear pooling for fine-grained classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 365–374 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Li, Y., Wang, N., Liu, J., Hou, X.: Demystifying neural style transfer. arXiv preprint arXiv:1701.01036 (2017)
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1449–1457 (2015)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Manohar, N., Kumar, Y.S., Kumar, G.H.: Supervised and unsupervised learning in animal classification. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 156–161. IEEE (2016)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. Neural Comput. 12(6), 1247–1283 (2000)
Teng, J., Zhang, D., Lee, D.J., Chou, Y.: Recognition of Chinese food using convolutional neural network. Multimedia Tools Appl. 78(9), 11155–11172 (2019)
Veech, J.: A comparison of landscapes occupied by increasing and decreasing populations of grassland birds. Conservation Biol.: J. Soc. Conservation Biol. 20, 1422–1432 (2006)
Wang, Y., Morariu, V.I., Davis, L.S.: Learning a discriminative filter bank within a CNN for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4148–4157 (2018)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)
Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2251–2265 (2018)
Yu, C., Zhao, X., Zheng, Q., Zhang, P., You, X.: Hierarchical bilinear pooling for fine-grained visual recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 595–610. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_35
Yuan, D.: Research on animal recognition algorithm based on convolutional neural network. Master’s thesis, South China University of Technology (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, B., Huang, W., Huang, Y., Yang, C., Xu, F. (2021). MPNet: A Multiprocess Convolutional Neural Network for Animal Classification. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_42
Download citation
DOI: https://doi.org/10.1007/978-981-16-2540-4_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2539-8
Online ISBN: 978-981-16-2540-4
eBook Packages: Computer ScienceComputer Science (R0)
