基于图像云模型语义标注的条件生成对抗网络

doi:10.16451/j.cnki.issn1003-6059.201804009

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Abstract As the missing information in the image is increasing, the existing methods extracting information from only a single image can not produce satisfactory completion results. Therefore, an automatic label conditional generative adversarial network(CGAN) based on image semantic is presented from the perspective of multi-granular cognition. It can be applied on image denoising and image completion. Firstly, the multi-layer semantic information from unlabeled images based on the Gaussian cloud transform algorithm is extracted. Then, the original images are segmented and the segmented images are labeled automatically in accordance with different granular semantic information. Furthermore, different granular segmented images and their labels are used as the training samples in the CGAN to get an image probability generation model, respectively. The large missing regions from a single image are completed based on the similar image generated by cloud semantic and CGAN. On the datasets of Caltech-UCSD Birds and Oxford-102flowers, the proposed model achieves the high performance in image denoising and image completion.

Key words： Cloud Model Automatic Semantic Annotation Generative Adversarial Network(GAN) Multi-granularity Cognitive Computing

Received: 14 August 2017

ZTFLH:

TP 391.41

Fund:Supported by National Natural Science Foundation of China(No.61572091)

Corresponding Authors: DU Qiuping(Corresponding author), master student. His research interests include could model, image processing and deep learning.

About author:: LIU Qun, Ph.D., professor. Her research interests include nonlinear dynamics, artificial neural network and complex networks.

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Cite this article:

DU Qiuping,LIU Qun. Conditional Generative Adversarial Network Based on Image Semantic Annotation of Cloud Model[J]. , 2018, 31(4): 379-388.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201804009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I4/379

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