语义自编码结合关系网络的零样本图像识别算法

doi:10.16451/j.cnki.issn1003-6059.201903003

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Abstract A semantic auto-encoder structure improved by relation network is proposed and used for zero sample identification algorithm to handle the projection domain shift problem and improve the robustness of distance similarity measure in the traditional model of zero-shot recognition. The feature map between image visual features and semantic vectors is constructed by the proposed algorithm based on the semantic auto-encoder, and then the reconstructed vector is sent to the neural network after concatenating the true value of the corresponding vector. Finally, the prediction category is determined by the output scalar. The experimental results show that compared with the traditional distance measurement method, the recognition rate of the proposed algorithm on the public datasets AWA, CUB and ImageNet-2 is improved and its semantic-visual projection has a better effect than back projection on some datasets.

Key words： Semantic Auto-Encoder Relation Network Zero-Shot Recognition Semantic Vector Projection Domain ShiftCitation

Received: 25 October 2018

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No.61501147), Natural Science Foundation of Heilongjiang Province(No.F2015040), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(No.2018203)

Corresponding Authors: LIN Kezheng, Ph.D., professor. His research interests include image processing, machine vision and pattern recognition.

About author:: (LI Haotian, master student. His research interests include machine learning and computer vision.)
(BAI Jingxuan, master student. Her research interests include image processing and computer vision.)
(LI Ao, Ph.D., lecturer. His research interests include sparse representation, image restoration and computer vision.)

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Cite this article:

LIN Kezheng,LI Haotian,BAI Jingxuan等. Zero-Shot Image Recognition Algorithm via Semantic Auto-Encoder Combining Relation Network[J]. , 2019, 32(3): 214-224.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201903003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I3/214

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