【摘要】 针对计算机生成图像(Computer Generated images, CG)与真实照片(Photograpgh, PG)识别率不高的问题,该文提出了一种改进的卷积神经网络方法来实现CG与PG的识别.该方法首先对识别问题进行卷积神经网络二分类建模,并选择VGG-19网络结构作为基础,建立不同的模型.该方法创新性地引入迁移学习,节省训练时间和大量计算资源,最后使用softmax分类器进行分类.实验结果表明,该文方法对PG图像的识别准确率达到92%.与其他方法比较,该文方法识别准确率最高,说明该文方法具有可行性与有效性.更多还原

【Abstract】 In order to solve the problem low recognition rate for Computer Generated images(CG) and Photographs(PG), an improved convolution neural network method is proposed to realize the recognition of CG and PG. This method first set up the two-classification model of the convolution neural network for the recognition problem and the VGG-19 network structure is selected as the basis to establish different models. This method innovatively introduces migration learning and saves training time and massive computing resources. Finally, softmax classifier is used to classify. The experimental results show that the accuracy of the proposed method for PG image recognition is up to 92%, and the recognition speed is faster. Compared with other methods, the method has the highest recognition accuracy and demonstrates the feasibility and effectiveness of the proposed method.更多还原