ABSTRACT
Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional hamming space, has attracted intensive attention in recent years. This is motivated by the facts a) the multi-modal data is widespread, e.g., the web images on Flickr are associated with tags, and b) hashing is an effective technique towards large-scale high-dimensional data processing, which is exactly the situation of cross-media retrieval. Inspired by recent advances in deep learning, we propose a cross-media hashing approach based on multi-modal neural networks. By restricting in the learning objective a) the hash codes for relevant cross-media data being similar, and b) the hash codes being discriminative for predicting the class labels, the learned Hamming space is expected to well capture the cross-media semantic relationships and to be semantically discriminative. The experiments on two real-world data sets show that our approach achieves superior cross-media retrieval performance compared with the state-of-the-art methods.
- Y. Bengio. Learning deep architectures for ai. Foundations and trends in Machine Learning, 2(1):1--127, 2009. Google ScholarDigital Library
- M. Bronstein, A. Bronstein, F. Michel, and N. Paragios. Data fusion through cross-modality metric learning using similarity-sensitive hashing. In CVPR, pages 3594--3601, 2010.Google ScholarCross Ref
- S. Kumar and R. Udupa. Learning hash functions for cross-view similarity search. In IJCAI, pages 1360--1365, 2011. Google ScholarDigital Library
- J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. Multimodal deep learning. In ICML, pages 689--696, 2011.Google ScholarDigital Library
- N. Srivastava and R. Salakhutdinov. Multimodal learning with deep boltzmann machines. In NIPS, pages 2222--2230, 2012.Google ScholarDigital Library
- L. Van der Maaten and G. Hinton. Visualizing data using t-sne. JMLR, 9(11), 2008.Google Scholar
- Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, 2008.Google ScholarDigital Library
- Z. Yu, F. Wu, Y. Yang, Q. Tian, J. Luo, and Y. Zhuang. Discriminative coupled dictionary hashing for fast cross-media retrieval. In SIGIR, pages 395--404, 2014. Google ScholarDigital Library
- Y. Zhen and D. Yeung. A probabilistic model for multimodal hash function learning. In SIGKDD, 2012. Google ScholarDigital Library
- X. Zhu, Z. Huang, H. T. Shen, and X. Zhao. Linear cross-modal hashing for efficient multimedia search. In ACM MM, pages 143--152, 2013. Google ScholarDigital Library
Index Terms
- Cross-Media Hashing with Neural Networks
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