Inductive Transfer Deep Hashing for Image Retrieval

ABSTRACT

With the explosive increase of online images, fast similarity search is increasingly critical for large scale image retrieval. Several hashing methods have been proposed to accelerate image retrieval, a promising way is semantic hashing which designs compact binary codes for a large number of images so that semantically similar images are mapped to similar codes. Supervised methods can handle such semantic similarity but they are prone to overfitting when the labeled data is few or noisy. In this paper, we concentrate on this issue and propose a novel Inductive Transfer Deep Hashing (ITDH) approach for semantic hashing based image retrieval. A transfer deep learning algorithm has been employed to learn the robust image representation, and the neighborhood-structure preserved method has been used to mapped the image into discriminative hash codes in hamming space. The combination of the two techniques ensures that we obtain a good feature representation and a fast query speed without depending on large amounts of labeled data. Experimental results demonstrate that the proposed approach is superior to some state-of-the-art methods.

References

1. A. Gionis, P. Indyk, R. Motwani, et al. Similarity search in high dimensions via hashing. In VLDB,volume 99, pages 518--529, 1999.
2. G. Hinton and R. Salakhutdinov. Discovering binary codes for documents by learning deep generative models. Topics in Cognitive Science, 3(1):74--91, 2011.
3. G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science,313(5786):504--507, 2006.
4. A. Krizhevsky and G. E. Hinton. Using very deep autoencoders for content-based image retrieval. In ESANN. Citeseer, 2011.
5. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, volume 1, page 4, 2012.
6. P. Li, M. Wang, J. Cheng, C. Xu, and H. Lu. Spectral hashing with semantically consistent graph for image indexing. Multimedia, IEEE Transactions on,15(1):141--152, 2013.
7. M. Norouzi and D. M. Blei. Minimal loss hashing for compact binary codes. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 353--360, 2011.
8. R. Salakhutdinov and G. Hinton. Semantic hashing. International Journal of Approximate Reasoning,50(7):969--978, 2009.
9. G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 750--757. IEEE, 2003.
10. R. Socher, B. Huval, B. P. Bath, C. D. Manning, and A. Y. Ng. Convolutional-recursive deep learning for 3d object classification. In NIPS, pages 665--673, 2012.
11. Z. Tang, S. Wang, X. Zhang, W. Wei, and S. Su. Robust image hashing for tamper detection using non-negative matrix factorization. Journal of Ubiquitous Convergence Technology, 2(1):pp--18, 2008.
12. J. Wang, S. Kumar, and S.-F. Chang. Semi-supervised hashing for scalable image retrieval. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 3424--3431. IEEE, 2010.
13. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, volume 9, page 6, 2008.
14. D. Zhang, J. Wang, D. Cai, and J. Lu. Self-taught hashing for fast similarity search. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 18--25. ACM, 2010.

Index Terms

• Information systems

  • Information retrieval

    • Information retrieval query processing

    • Retrieval models and ranking