基于集成分类的暴恐图像自动标注方法

Violent image annotation using ensemble learning

为减少暴恐图像对社会发展和青少年成长造成的不利影响,本文提出一种基于集成分类的暴恐图像自动标注方法,辅助筛除网页中的暴恐信息。该方法将暴恐图像的标注视作多标签分类问题,利用迁移学习训练多个子网络,然后通过集成学习对子网络的输出进行融合,同时在融合过程中针对各个标签在不同网络上的准确率进行权重分配,最后经过一系列矩阵运算得到图像的标注结果。实验结果表明,与传统机器学习算法相比,本文方法在准确率和召回率上都有较大提升,并改善了样本不均衡所造成的不同标签类别上模型标注精确度差异较大的问题。

In order to reduce the negative impact of the horror image on social development and adolescent growth, a violent image annotation algorithm based on ensemble learning is proposed, assisting in screening out the horror information in the webpage. The annotation of violent image is considered as a multi-label classification problem in this method. Multiple sub-networks are trained through transfer learning, and then the ensemble learning is introduced to fuse the outputs of sub-networks. In the process of fusion, weights are allocated according to the precision of each label on different networks, thus the annotation result is obtained through a series of matrix operations. The experimental results show that the proposed method achieves a great improvement in precision and recall than traditional machine learning algorithm, and also improves the problem that the precision of model annotation on different labels varies greatly due to the label category imbalance.