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Deep Enhanced Weakly-Supervised Hashing With Iterative Tag Refinement
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DOI：10.1109/TMM.2021.3087356.png)
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摘要
En 中文
On image-sharing websites, images are usually associated with user-generated tags which contain semantic information and are more easily accessible than accurate labels. It is beneficial to utilize such tags as supervised information to learn image feature representation. However, some tags are not related with the image content and disturb the feature learning process. In this paper, we are dedicated to refining such noisy tags and upgrading the image feature learning. To this end, we propose a novel deep enhanced weakly-supervised hashing method, in which tags are adaptively refined according to image content. In our approach, we first map the deep image feature representation into the tag embedding space, and learn the discriminative as well as compact feature representations with the corresponding tags. After that, by referring to the learned feature representation in the first step, we refine the tags to become consistent with image content. The above two steps are alternated until convergence. Finally, we can obtain more content-relevant tags, better image features and binary hashing functions. The experiments on two image datasets prove that the proposed method outperforms the state-of-the-art weakly-supervised deep hashing methods on image retrieval task.
Keyword:
Image retrieval
Quantization (signal)
Semantics
Noise measurement
Binary codes
Insects
Neural networks
Enhanced weakly-supervised hashing
deep hashing
image retrieval
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