Improving Semantic Autoencoder Zero-Shot Classification Algorithm by Metric Learning

doi:10.13190/j.jbupt.2018-010

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (4): 69-75.doi: 10.13190/j.jbupt.2018-010

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Improving Semantic Autoencoder Zero-Shot Classification Algorithm by Metric Learning

CHEN Xiang-feng^1,2, CHEN Wen-bai^1,2

1. College of Automation, Beijing Information Science and Technology University, Beijing 100192, China;
2. MOE Key Laboratory of Machine Perception, Peking University, Beijing 100871, China

Received:2018-01-11 Online:2018-08-28 Published:2018-10-09

Abstract

Abstract: To improve the robustness of similarity metric method in zero-shot learning, a new metric learning for zero-shot image classification was introduced. It is composed of autoencoders, which can learn the optimal metric function in the feature-aligned semantic embedding space. The similarity between test sample features and the semantic features of the class labels can be calculated by metric function, predicting the label of the class by the neighboring method. Thus, the classification error caused by inappropriate distance function is prevented. Compared with the traditional distance metric algorithm, the experiments show that the proposed method reduces the recognition error rate; the recognition accuracy is improved to 94.7%, 63.7% and 28.59% on the AWA, CUB and ImNet-2 datasets. At the same time, it was confirmed that the recognition accuracy of the semantic-visual mapping direction was 2.5%~10.1% higher than the opposite direction.

Key words: zero-shot classification, metric learning, semantic autoencoder, semantic embedding space, distance function

CLC Number:

TP18

CHEN Xiang-feng, CHEN Wen-bai. Improving Semantic Autoencoder Zero-Shot Classification Algorithm by Metric Learning[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(4): 69-75.

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Improving Semantic Autoencoder Zero-Shot Classification Algorithm by Metric Learning

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