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| Feature Fusion Based Image Retrieval Using Deep Learning |
| Qingyong Xu, Shunliang Jiang, Wei Huang, Famao Ye, Shaoping Xu |
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Abstract In the last decades, Content Based Image Retrieval and image
classification have become popular, and among them Region-based
Image Retrieval is quite active. More and more descriptors and
retrieval methods have been proposed and investigated in order to
improve the retrieval performance. This paper proposed a feature
fusion deep learning method. The features including colors, texture
and shape, which are extracted from both the entire image and
regions. The features are then trained using diverse deep learning
methods. The conducted deep learning methods include Sparse
Auto-encoders, Denoising Auto-encoding, Deep Belief Nets, Drop Out
Neural Networks, and Deep Boltzmann Machine. The method is evaluated
through extensive experiments on Corel 10K datasets. Experimental
results demonstrate that the introduced methods are comparable with
the state-of-arts in this image retrieval application.
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| DOI:10.12733/jics20105681 |
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