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Labeling Images by Integrating Sparse Multiple Distance Learning and Semantic Context Modeling

机译：通过集成稀疏的多远程学习和语义上下文建模来标记图像

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摘要

Recent progress on Automatic Image Annotation (AIA) is achieved by either exploiting low level visual features or high level semantic context. Integrating these two paradigms to further leverage the performance of AIA is promising. However, very few previous works have studied this issue in a unified framework. In this paper, we propose a unified model based on Conditional Random Fields (CRF), which establishes tight interaction between visual features and semantic context. In particular, Kernelized Logistic Regression (KLR) with multiple visual distance learning is embedded into the CRF framework. We introduce L_1 and L_2 regularization terms into the unified learning process for the distance learning and the parameters penalty respectively. The experiments are conducted on two benchmarks: Corel and TRECVID-2005 data sets for evaluation. The experimental results show that, compared with the state-of-the-art methods, the unified model achieves significant improvement on annotation performance and shows more robustness with increasing number of various visual features.
机译：通过利用低级别的视觉功能或高级的语义上下文，可以实现自动图像注释（AIA）的最新进展。整合这两个范例以进一步利用AIA的性能是有希望的。但是，很少有以前的著作在统一框架中研究过此问题。在本文中，我们提出了一个基于条件随机场（CRF）的统一模型，该模型建立了视觉特征与语义上下文之间的紧密交互。特别是，具有多重视觉远程学习功能的内核逻辑回归（KLR）已嵌入到CRF框架中。我们将L_1和L_2正则化项分别引入到远程学习和参数惩罚的统一学习过程中。实验在两个基准上进行：Corel和TRECVID-2005数据集进行评估。实验结果表明，与最新方法相比，统一模型在注释性能上有显着提高，并且随着各种视觉特征数量的增加，其显示出了更高的鲁棒性。

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