1. 面向 Web 图像检索的基于语义迁移的 无监督深度哈希.
- Author
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许 胜, 陈盛双, and 谢 良
- Subjects
- *
IMAGE retrieval , *HASHING , *DEEP learning , *CIPHERS , *IMAGE , *HAMMING distance , *SEMANTICS - Abstract
Most existing Web image retrieval approaches only consider visual features. They ignore the valuable semantics involved in the associated texts,and fail to take advantages of text. This paper proposed a new unsupervised visual hashing approach called STDVH. Firstly, it extracted the semantic information of the training text by spectral clustering. Then, it constructed a deep convolutional neural network to transfer the text semantic information into the learning of the image hash code. At last, it trained the image hash codes and hash functions in a unified framework, and completed the effective retrieval of largescale image data in low-dimensional Hamming space. Experiments on two publicly available image datasets Wiki and MIR Flickr indicate that the proposed approach can achieve superior performance over other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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