Deep Co-Image-Label Hashing for Multi-Label Image Retrieval
- Publisher:
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Multimedia, 2022, 24, pp. 1116-1126
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Deep Co-Image-Label Hashing for Multi-Label Image Retrieval.pdf | Published version | 2.09 MB |
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Deep supervised hashing has greatly improved retrieval performance with the powerful learning capability of deep neural network. In multi-label image retrieval, existing deep hashing simply indicates whether two images are similar by constructing a similarity matrix. However, it ignores the dependency among multiple labels that has been shown important in multi-label application. To fulfill this gap, this paper proposes Deep Co-Image-Label Hashing (DCILH) to discover label dependency. Specifically, DCILH regards image and label as two views, and maps the two views into a common deep Hamming space. DCILH proposes to learn prototype for each label, and preserve similarity among images, labels, and prototypes. To exploit label dependency, DCILH further employs the label-correlation aware loss on the predicted labels, such that predicted output on positive label is enforced to be larger than that on negative label. Extensive experiments on several multi-label benchmarks demonstrate the proposed DCILH outperforms state-of-the-art deep supervised hashing on large-scale multi-label image retrieval.
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