Abstract
In order to address the problem that numerical labels are difficult to optimize, one-hot encoding is introduced into image classification tasks, and has been widely used in current models based on CNNs. However, one-hot encoding neglects the textual semantics of class labels, which closely relate to image characteristics and contain latent connections between images. Inspired by distributional similarity based representations in Natural Language Processing society, we propose a framework by introducing Word2Vec into classic CNN models to improve image classification performance. By mining the latent semantic power of classes labels, word vector representations participate in the classification model instead of the traditional one-hot encoding. In the evaluation experiments implemented on data sets of CIFAR-10 and CIFAR-100, a series of representative CNNs have been tested as the feature extraction component for our framework. Experimental results show that the proposed method has revealed compelling ability to improve the classification accuracy.
This research was supported by Project 61672474, 61501412 supported by NSFC. National Science and Technology Major Project (No. 2017ZX05036-001-010). Science and Technology Planning Project of Guangdong Province, China. (No. 2018B020207012).
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
Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for image classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1425–1438 (2016)
Bengio, Y.: Neural net language models. Scholarpedia 3(1), 3881 (2008)
Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)
Chollet, F., et al.: Keras (2015)
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)
De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)
Dixit, M., Chen, S., Gao, D., Rasiwasia, N., Vasconcelos, N.: Scene classification with semantic fisher vectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2974–2983 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. CoRR abs/1502.01852 (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)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization (2015)
Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123(1), 32–73 (2017)
Krizhevsky, A., Nair, V., Hinton, G.: The CIFAR-10 dataset (2014). http://www.cs.toronto.edu/kriz/cifar.html
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)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 43(1), 29–44 (2001)
Li, F.F., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594–611 (2006)
Li, X., Liao, S., Lan, W., Du, X., Yang, G.: Zero-shot image tagging by hierarchical semantic embedding. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 879–882. ACM (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. CoRR abs/1312.4400 (2013)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Joulin, A., Chopra, S., Mathieu, M., Ranzato, M.: Learning longer memory in recurrent neural networks. arXiv preprint arXiv:1412.7753 (2014)
Morgado, P., Vasconcelos, N.: Semantically consistent regularization for zero-shot recognition. In: CVPR, vol. 9, p. 10 (2017)
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Invest. 30(1), 3–26 (2007)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, May 2010. http://is.muni.cz/publication/884893/en
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856–3866 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Null, p. 1470. IEEE (2003)
Su, Y., Jurie, F.: Improving image classification using semantic attributes. Int. J. Comput. Vis. 100(1), 59–77 (2012)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for Twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1555–1565 (2014)
Tieleman, T., Hinton, G.: Rmsprop. Lecture, COURSERA (2012)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 652–663 (2017)
Wan, J., et al.: Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 157–166. ACM (2014)
Zhang, L., Xiang, T., Gong, S., et al.: Learning a deep embedding model for zero-shot learning (2017)
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
Jia, H., Yao, H., Tian, T., Yan, C., Li, S. (2019). The Latent Semantic Power of Labels: Improving Image Classification via Natural Language Semantic. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-37429-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37428-0
Online ISBN: 978-3-030-37429-7
eBook Packages: Computer ScienceComputer Science (R0)