Abstract
Zero-shot classification (ZSC) is a hot topic of computer vision. Because the training labels are totally different from the testing labels, ZSC cannot be dealt with by classical classifiers. Attribute-based classifier is a dominant solution for ZSC. It employs attribute annotations to bridge training labels and testing labels, making it able to realize ZSC. Classical attribute-based classifiers treat different attributes equally. However, the attributes contribute to classification unequally. In this paper, a novel attribute-based classifier for ZSC named HCRL is proposed. HCRL utilizes hierarchical clustering to obtain a hierarchy from the attribute annotations. Then the attribute annotations are decomposed into hierarchical rules which contain only a few attributes. The discriminative abilities of the rules reflect the significances of attributes to classification, but there are no training samples for evaluating the rules. The discriminative abilities are determined by reinforcement learning during the testing and the most discriminative rules are picked out for classification. Experiments conducted on 2 popular datasets for ZSC show the competitiveness of HCRL.
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
Zhang, Z., Saligrama, V.: Zero-shot learning via semantic similarity embedding. In: IEEE International Conference on Computer Vision, pp. 4166–4174 (2015)
Socher, R., Ganjoo, M., Sridhar, H., Bastani, O., Manning, C.D., Ng, A.Y.: Zero-shot learning through cross-modal transfer. In: Advances in Neural Information Processing Systems, pp. 935–943 (2013)
Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36, 453–465 (2014)
Ji, Z., Yu, Y., Pang, Y., Guo, J., Zhang, Z.: Manifold regularized cross-modal embedding for zero-shot learning. Inf. Sci. 378, 48–58 (2017)
Changpinyo, S., Chao, W.L., Gong, B., Sha, F.: Synthesized classifiers for zero-shot learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5327–5336 (2016)
Alhalah, Z., Tapaswi, M., Stiefelhagen, R.: Recovering the missing link: predicting class-attribute associations for unsupervised zero-shot learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5975–5984 (2016)
Larochelle, H., Erhan, D., Bengio, Y.: Zero-data learning of new tasks. In: AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July, pp. 646–651 (2008)
Rohrbach, M., Stark, M., Schiele, B.: Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In: Computer Vision and Pattern Recognition, pp. 1641–1648 (2011)
Wang, X., Chen, C., Cheng, Y., Wang, Z.J.: Zero-shot image classification based on deep feature extraction. IEEE Trans. Cogn. Dev. Syst. 99, 1–14 (2017)
Hoo, W.L., Chan, C.S.: Recognizing unknown objects with attributes relationship model. Expert Syst. Appl. 42, 9279–9283 (2015)
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1778–1785 (2009)
Alhalah, Z., Stiefelhagen, R.: How to transfer? Zero-Shot object recognition via hierarchical transfer of semantic attributes. In: IEEE Winter Conference on Applications of Computer Vision, pp. 837–843 (2015)
Kivinen, J., Smola, A.J., Williamson, R.C.: Learning with Kernels. MIT Press, Cambridge (2002)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., Waltham (2011)
Rafsanjani, M.K., Varzaneh, Z.A., Chukanlo, N.E.: A survey of hierarchical clustering algorithms. Int. J. Appl. Math. Comput. Sci. 5, 229–240 (2012)
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015)
Mozer, S.: C, M., Hasselmo, M.: Reinforcement Learning: An Introduction. IEEE Trans. Neural Networks 16, 285–286 (1998)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: National Conference on Artificial Intelligence, pp. 381–388 (2006)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (2014)
Keerthi, S., Shevade, S., Bhattacharyya, C., Murthy, K.: Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput. 13, 637–649 (2006)
Qiao, L., Tuo, H., Fang, Z., Feng, P., Jing, Z.: Joint probability estimation of attribute chain for zero-shot learning. In: IEEE International Conference on Image Processing, pp. 1863–1867 (2016)
Acknowledgments
The presented work is framed within the National Natural Science Foundation of China (No. 71371184).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, B., Yao, L., Wu, J., Feng, X. (2017). Employing Hierarchical Clustering and Reinforcement Learning for Attribute-Based Zero-Shot Classification. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-69179-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69178-7
Online ISBN: 978-3-319-69179-4
eBook Packages: Computer ScienceComputer Science (R0)