With the popularity of multi-modal data on Web, cross media retrieval has become a hot research topic. Existing cross modal hash methods assume that there is a latent space shared by multi-modal features, and embed the heterogeneous data into a joint abstraction space by linear projections. However, these approaches are sensitive to the noise of data, and unable to make use of unlabelled data and multi-modal data with missing values in the real-world applications. To address these challenges, in this paper, we propose a novel Multi-modal Deep Learning based Hashing (MDLH) algorithm. In particular, MDLH adopts deep neural network to encode heterogeneous features into a compact common representation and learn the hash functions based on the common representation. The parameters of the whole model are fine-tuned in supervised training stage. Experiments on two standard datasets show that our method achieves more effective results than other methods in cross modal retrieval.
CITATION STYLE
Qu, W., Wang, D., Feng, S., Zhang, Y., & Yu, G. (2015). A novel cross modal hashing algorithm based on multi-modal deep learning. In Communications in Computer and Information Science (Vol. 568, pp. 156–167). Springer Verlag. https://doi.org/10.1007/978-981-10-0080-5_14
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