JACIII Vol.22 No.5 pp. 611-620
doi: 10.20965/jaciii.2018.p0611


Modality-Reconstructed Cross-Media Retrieval via Sparse Neural Networks Pre-Trained by Restricted Boltzmann Machines

Bin Zhang, Huaxiang Zhang, Jiande Sun, Zhenhua Wang, Hongchen Wu, and Xiao Dong

Department of Computer Science, Shandong Normal University
No. 1, University Road, Changqing District, Jinan 250300, China

Corresponding author

August 24, 2017
May 16, 2018
September 20, 2018
cross-media retrieval, restricted Boltzmann machines, sparse neural networks, modality-reconstructed

Cross-media retrieval has raised a lot of research interests, and a significant number of works focus on mapping the heterogeneous data into a common subspace using a couple of projection matrices corresponding to each modal data before implementing similarity comparison. Differently, we reconstruct one modal data (e.g., images) to the other one (e.g., texts) using a model named sparse neural network pre-trained by Restricted Boltzmann Machines (MRCR-RSNN) so that we can project one modal data into the space of the other one directly. In the model, input is low-level features of one modal data and output is the other one. And cross-media retrieval is implemented based on the similarities of their representatives. Our model need not any manual annotation and its application is more widely. It is simple but effective. We evaluate the performance of our method on several benchmark datasets, and experimental results prove its effectiveness based on the Mean Average Precision (MAP) and Precision Recall (PR).

Modality-reconstructed cross-media retrieval

Modality-reconstructed cross-media retrieval

Cite this article as:
B. Zhang, H. Zhang, J. Sun, Z. Wang, H. Wu, and X. Dong, “Modality-Reconstructed Cross-Media Retrieval via Sparse Neural Networks Pre-Trained by Restricted Boltzmann Machines,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.5, pp. 611-620, 2018.
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Last updated on Jul. 23, 2024