一种基于迁移学习的文本—图像特征映射算法

A Transfer Learning Based Text-Image Feature Mapping Algorithm

提出一种事件约束下基于迁移学习的文本—图像特征映射算法.通过潜在狄利克莱分配方法对事件文本数据进行主题建模,并通过计算主题特征的信息增益选出最显著的文本特征;用视觉词袋模型和朴素贝叶斯方法对事件图片进行主题建模;通过同事件下的文本数据特征分布和文本—图像共现数据特征分布,实现了对图像特征分布的近似.在包含15个主题事件的数据集上进行实验的结果证明了所提特征映射算法的有效性.

A transfer learning based text-image feature mapping algorithm under event constraint is proposed. Firstly, the documents of each event are modeled by the latent dirichlet allocation, in which the most discriminating feature is obtained by computing the information gain of each topic. Secondly, the images of the corresponding event are modeled through the bag-of-visual-word model and the na＇fve hayes approach. Finally, the feature distributions of the target images are approximated by utilizing the feature distributions of the text data and the text-image co-occurrence data within the same event. Experiment is conducted on a dataset containing 15 categories of events. The effectiveness of the proposed feature mapping algorithm is shown.