| Citation: | LIAN Lianrong, XIANG Xinguang. Social image tag refinement and annotation based on noise Cauchy distribution[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 632-640. doi: 10.13700/j.bh.1001-5965.2020.0454(in Chinese)
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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.
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