Random Field Model for Integration of Local Information and Global Information
Random Field Model for Integration of Local Information and Global Information
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DOI：
10.1109/tpami.2008.105
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发表时间：
2008-08
影响因子：
23.6
通讯作者：
Takahiro Toyoda;O. Hasegawa
中科院分区：
文献类型：
--
作者：
Takahiro Toyoda;O. Hasegawa
本文提出了一个通用的框架，显式建模的局部信息和全局信息的条件随机场的建议。该方法提取全局图像特征以及局部图像特征，并使用它们来预测输入图像的场景。基于预测场景生成基于场景的自上而下信息。它表示图像上标签和类别兼容性的全局空间配置。全局信息的结合有助于解决局部模糊性，并实现局部和全局一致的图像识别。尽管该模型的简单，所提出的方法在两个数据集的图像标记表现出良好的性能。
This paper presents a proposal of a general framework that explicitly models local information and global information in a conditional random field. The proposed method extracts global image features as well as local ones and uses them to predict the scene of the input image. Scene-based top-down information is generated based on the predicted scene. It represents a global spatial configuration of labels and category compatibility over an image. Incorporation of the global information helps to resolve local ambiguities and achieves locally and globally consistent image recognition. In spite of the model's simplicity, the proposed method demonstrates good performance in image labeling of two datasets.