Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion

Huanting Wang; Bo Qu; Xiaoqiang Lu; Yaxiong Chen

doi:10.1117/12.2573106

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12 June 2020 Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion

Huanting Wang, Bo Qu, Xiaoqiang Lu, Yaxiong Chen

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Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115191I (2020) https://doi.org/10.1117/12.2573106
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Hashing technology is widely used to solve the problem of large-scale Remote Sensing (RS) image retrieval due to its high speed and low memory. Among the existing hashing algorithm, the unsupervised method is widely used in largescale RS image retrieval. However, the existing unsupervised RS image retrieval methods do not consider the multichannel properties of multi-spectral RS images and the discriminability in the local preservation mapping process adequately, which make it difficult to satisfy the retrieval performance of RS data. To solve these problems, we propose an unsupervised Variational Auto-Encoder Hashing algorithm based on multi-channel feature fusion (VAEH). MultiChannel Feature Fusion (MCFF) is used to extract the feature information of image, which fully considers the multichannel properties of the multi-spectral RS image. In order to enhance the discriminability in the local preservation mapping process, variational construction process and automatic encoder are added into the learning process of hashing function, and the KL distance of the Variational Auto-Encoder (VAE) is used to constrain the hashing code. Experiments on two large public RS image data sets (i.e. SAT-4 and SAT-6) have shown that our VAEH method outperforms the state of the art.

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Huanting Wang, Bo Qu, Xiaoqiang Lu, and Yaxiong Chen "Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115191I (12 June 2020); https://doi.org/10.1117/12.2573106

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