In the field of image recognition, images with insufficient label data cannot be well recognized by the supervised method model. In order to solve this problem, a semi-supervised method model based on Generative Adversarial Network(GAN) was proposed. That is, by combining the advantages of semi-supervised GANs and deep convolutional GANs, and replacing the sigmoid activation function with softmax in the output layer, the Semi-Supervised Deep Convolutional GAN(SS-DCGAN) model was established. Firstly, the generated samples were defined as pseudo-samples and used to guide the training process. Secondly, the semi-supervised training method was adopted to update the parameters of the model. Finally, the recognition of abnormal(cerebral infarction) images was realized. Experimental results show that the SS-DCGAN model can recognize abnormal images well with little label data, which achieves 95.05% recognition rates. Compared with Residual Network 32(ResNet32) and Ladder networks, the SS-DCGAN model has significant advantages.

Stereoscopic 3D (S3D) image retargeting aims to adjust aspect ratio of S3D images. To objectively and accurately assess the quality of different retargeted S3D images, a retargeted S3D image quality assessment database was constructed. Firstly, 45 original images were retargeted by eight representative retargeting algorithms with two retargeting scales to generate 720 retargeted S3D images. Then, the subjective quality evaluation score of each retargeted image was obtained via subjective testing. Finally, the subjective scores were converted to MOS (Mean Opinion Score) values. Based on all above, an objective quality assessment method was proposed for retargeted S3D images. In this method, three types of features including depth perception, visual comfort and image quality of left and right views were extracted to calculate the retargeted S3D image quality with the use of support vector regression prediction. Experimental results on the proposed database show that the proposed method has the Pearson linear correlation coefficient and the Spearman rank-order correlation coefficient higher than 0.82 and 0.81 respectively, demonstrating its superiority in retargeted S3D image visual quality assessment.