基于噪声柯西分布的社交图像标签优化与标注

随着社交网络的快速发展，带有用户提供标签的社交网络图像呈现爆炸式增长。但是用户提供的标签是不准确的，存在很多不相关以及错误的标签。这势必会增加相关多媒体任务的困难。针对标签噪声无序性以及常用的高斯分布对标签噪声中大噪声过于敏感的问题，但是高斯分布对大噪声比较敏感。鉴于此，采用对各种噪声都具有鲁棒性的柯西分布拟合噪声，提出了一个基于噪声柯西分布的弱监督非负低秩深度学习（CDNL）模型，通过柯西分布建模标签噪声来获得理想标签，并利用深度神经网络模块学习视觉特征和理想标签之间的内在联系，来得到图像对应的正确标签，从而大幅提高社交网络图像的标签准确率。所提模型不仅可以修正错误标签、补充缺失标签，也可以对新图像进行标注。在2个公开的社交网络图像数据集上进行了验证，并且与一些最新的相关工作进行了对比，证实了所提模型的有效性。 

Social image tag refinement and annotation based on noise Cauchy distribution

With the rapid development of social networks, images with social tags have increased explosively. However, these tags are usually inaccurate and irrelevant which will make it harder for the relevant multimedia tasks. Although label noise is chaotic and disordered, it still conforms to a certain probability distribution. Most of the current methods use Gaussian distribution to fit the noise, but Gaussian distribution is very sensitive to large noise. Thus we use the Cauchy distribution to fit the noise, which is robust to various noises. In this paper, we propose a weakly-supervised Non-negative Low-rank deep learning model based on Cauchy Distribution (CDNL), which builds the noise model by Cauchy distribution to obtain the ideal label and uses deep neural network to reveal the intrinsic connection between the visual features of the image and the ideal labels. The proposed method can not only correct wrong labels and add missing labels, but also tag new images. Experiments are conducted on two public social network image datasets. Compared with some of the latest related work, the results show the effectiveness of the proposed method.