基于语义链接网的图像自动标注

Image Auto-Annotation Using Semantic Link Network

自动图像标注是机器视觉领域中的一项具有挑战性的课题。大多数传统方法聚集在视觉图像与标注概念间的建模,而忽略了语义概念之间的关系。本文提出了一种新的标注方法IA-SLN,它能有效捕获语义概念关系从而提升图像标注性能。首先,构建了一个基于全局关联语义与局部关联语义学习的语义链接网。其次,利用它标注未标注图像。当一个标注概念与其关联概念们频繁共现于图像中,IA-SLN方法将提升该标注概念的语义预测值。最后,通过一个标注增强新策略获得未标注图像的最相关语义标注。在IAPR公共数据集上对比了其他方法,实验结果表明我们的图像标注方法IA-SLN性能更优。

Image Auto-annotation remains as a challenge in machine vision.Most conventional approaches concentrate on the relations between visual images and labeled concepts,and neglect the correla-tions between semantic concepts.In this paper,we present a novel approach called Image Au-to-annotation using Semantic Link Network(IA-SLN),which can effectively capture semantic cor-relations to boost image auto-annotation.Specifically,we first construct a semantic link network based on the global concept correlations and the local concept correlations.Second,we utilize it to annotate unlabeled images.When a concept and its associated concepts frequently occur in images,the prediction of this concept will be boosted in our IA-SLN.Finally,we obtain the most relevant annotations for each of unlabeled images by using a new strategy of annotation promotion.The experimental results on the publicly available dataset IAPR have shown that our approach performs favourably compared with several other state-of-the-art methods.