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深度哈希二值编码
Our model is learned under
three constraints at the top layer of the deep network:
1) the loss between the original real-valued feature descriptor
and the learned binary vector is minimized, 2) the binary
codes distribute evenly on each bit, and 3) different bits
are as independent as possible. To further improve the discriminative
power of the learned binary codes, we extend
DH into supervised DH (SDH) by including one discriminative
term into the objective function of DH which simultaneously
maximizes the inter-class variations and minimizes
the intra-class variations of the learned binary codes. Experimental
results show the superiority of the proposed approach
over the state-of-the-arts.
2018-07-17
hashing-baseline-for-image-retrieval-master
用于图像检索的哈希编码方法,(Various hashing methods for image retrieval and serves as the baselines )
2017-10-25
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