基于深度学习的图像自动标注算法

图像的自动标注是图像检索领域一项基础而又富有挑战性的任务。深度学习算法自提出以来在图像和文本识别领域取得了巨大的成功,是一种解决"语义鸿沟"问题的有效方法。图像标注问题可以分解为基于图像与标签相关关系的基本图像标注和基于标注词汇共生关系的标注改善两个过程。文中将基本图像标注问题视为一个多标记学习问题,图像的标签先验知识作为深度神经网络的监督信息。在得到基本标注词汇的基础上,利用原始图像标签词汇的依赖关系与先验分布改善了图像的标注结果。最后将所提出的改进的深度学习模型应用于Corel和ESP图像数据集,验证了该模型框架及所提出的解决方案的有效性。

Image Auto-Annotation Based on Deep Learning

Image auto-annotation is a basic and challenge task in the image retrieval work. The traditional machine learning methods have btained a lot achievements in this field. The deep learning algorithm has achieved great success in image and text learning work since it is presented, so it can be an efficient method to solve the semantic gap problems. Image auto-annotation can be decomposed into two steps, that is, the basic image auto-annotation based on the relationship between image and tag, and the annotation enhanced based on the mutual information of the tags. In this article, the basic image auto-annotation is viewed as a multi-labelled problem. Therefore the prior knowledge of the tags can be used as the supervise information of the deep neural network. After obtained the image tag s, the dependent relationship of the tags is used to improve the annotation result. Finally, the model is tested in Corel and ESP datasets, and results prove that the method can efficiently solve the image auto-annotation problems.