Shared Features Based Relative Attributes for Zero-shot Image Classification
QIAO Xue① PENG Chen① DUAN He① ZHANG Yuyao②
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)
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.
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