Shared Features Based Relative Attributes forZero-shot Image Classification

QIAO Xue; PENG Chen; DUAN He; ZHANG Yuyao

doi:10.11999/JEIT161133

Volume 39 Issue 7

Jul. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(7): 1563-1570

QIAO Xue, PENG Chen, DUAN He, ZHANG Yuyao. Shared Features Based Relative Attributes forZero-shot Image Classification[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1563-1570. doi: 10.11999/JEIT161133

Citation:

QIAO Xue, PENG Chen, DUAN He, ZHANG Yuyao. Shared Features Based Relative Attributes forZero-shot Image Classification[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1563-1570. doi: 10.11999/JEIT161133

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Shared Features Based Relative Attributes forZero-shot Image Classification

doi: 10.11999/JEIT161133

1.
(Suzhou Institute, Institute of Electronics, Chinese Academy of Sciences, Suzhou 215123, China)
2.
(School of Software Engineering, University of Science and Technology of China, Hefei 231000, China)

Funds:

The National Natural Science Foundation of China (41501485)

Received Date: 2016-10-25
Rev Recd Date: 2017-03-02
Publish Date: 2017-07-19

Abstract

Abstract

Most algorithms of the zero-shot image classification with relative attributes do not consider the relationship between attributes and classes, therefore a new relative attributes method based on shared features is proposed for zero-shot image classification. In analogy to the multi-task learning, the object classifier and attribute classifier are simultaneously learned in this method, from which a shared sub-space of lower dimensional features is obtained to mine the relationship between attributes and classes. Inspired by the success of shared features, a novel relative attributes model based on shared features is proposed to promote the performance of the relationship between attributes and classes, in which the ranking function per attribute is learned by using shared features. In addition, the novel relative attributes model based on shared features is applied to zero-shot image classification, which yields high accuracy due to the shared features included. Experimental results demonstrate that the proposed method can achieve high relative attributes learning efficiency and zero-shot image classification accuracy.
- Relative attribute,
- Multi-task learning,
- Shared features,
- Zero-shot image classification

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References(20)

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