Abstract
The techniques of feature extraction and representation on image data have been significantly progressed in recent years due to the development of deep learning. With a large number of representative image features being extracted from ImageNet by convolutional neural networks, many object recognizing applications were successfully accomplished effectively. In this paper, two supervised image retrieval models for retrieving images with similar hierarchical concept are investigated and compared. First, image features are extracted by pre-trained VGG convolutional networks. Then, the supervised retrieval models are learned from a set of images with hierarchical concept labels. The experimental results show that the hash-based model generally is superior to classifier-based model both in F1 measure and MAP no matter what in coarse level or fine level of concept hierarchy.
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References
Cao, Z., Long, M., Wang, J., Yu, P.S.: Hashnet: deep learning to hash by continuation. In: The IEEE International Conference on Computer Vision, pp. 5608–5617 (2017)
Guo, Y., Liu, Y., Bakker, E.M., Guo, Y., Lew, M.S.: CNN-RNN: a large-scale hierarchical image classification framework. Multimedia Tools Appl. 77(8), 1–21 (2018)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: International Conference on Very Large Data Bases, pp. 518–529. Morgan Kaufmann, Edinburgh (1999)
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)
Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: The IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 27–35 (2015)
Liu, H., Wang, R., Shan, S., Chen, X.: Deep supervised hashing for fast image retrieval. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 2064–2072 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Wan, J., et al.: Deep learning for content-based image retrieval: a comprehensive study. In: 22nd ACM International Conference on Multimedia, pp. 157–166 (2014)
Wang, D., et al.: Supervised deep hashing for hierarchical labeled data. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Wang, H., Cai, Y., Zhang, Y., Pan, H., Lv, W., Han, H.: Deep learning for image retrieval: what works and what doesn’t. In: IEEE International Conference on Data Mining Workshop, pp. 1576–1583 (2015)
Zhang, J., Peng, Y.: SSDH: semi-supervised deep hashing for large scale image retrieval. IEEE Trans. Circ. Syst. Video Tech. 29(1), 212–225 (2019)
Zhe, X., Ou-Yang, L., Chen, S., Yan, H.: Semantic hierarchy preserving deep hashing for large-scale image retrieval. arXiv preprint arXiv:1901.11259 (2019)
Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: International Conference on Artificial Intelligence, pp. 2415–2421. AAAI Press, Phoenix (2016)
Visual Object Classes Challenge (2012). http://host.robots.ox.ac.uk/pascal/VOC/voc2012
Keras: The Python Deep Learning library. https://keras.io
Acknowledgments
This research was funded in part by Ministry of Science and Technology of Taiwan, R. O. C. under contract MOST 106-2221-E-024-017.
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Chien, BC., Hsu, YC., Huang, YY. (2019). Supervised Deep Learning for Hierarchical Image Data Retrieval. In: Lin, JW., Ting, IH., Tang, T., Wang, K. (eds) Multidisciplinary Social Networks Research. MISNC 2019. Communications in Computer and Information Science, vol 1131. Springer, Singapore. https://doi.org/10.1007/978-981-15-1758-7_1
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DOI: https://doi.org/10.1007/978-981-15-1758-7_1
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