Automatic image retargeting
Automatic image retargeting
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DOI：
10.1145/1149488.1149499
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发表时间：
2005-12
期刊：
影响因子：
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通讯作者：
V. Setlur;Saeko Takagi;R. Raskar;Michael Gleicher;B. Gooch
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文献类型：
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作者：
V. Setlur;Saeko Takagi;R. Raskar;Michael Gleicher;B. Gooch
我们提出了一种非照片真实感算法，用于将大图像重定向到小尺寸显示器，特别是在移动设备上。此方法适用于大图像，因此当以较低的目标分辨率显示时，图像中的重要对象仍可识别。现有的图像处理技术，如裁剪，对于包含单个重要对象的图像效果很好，而下采样对于包含低频信息的图像效果很好。然而，当这些技术被自动应用于具有多个对象的图像时，图像质量下降并且可能丢失重要信息。我们的算法解决了图像中多个重要对象的情况。重定目标算法将图像分割成区域，识别重要区域，移除它们，填充产生的间隙，调整剩余图像的大小，并重新插入重要区域。我们的方法是基于一种既可理解又大小可变的视觉注意模型来构建图像的拓扑约束缩略，使该方法适合于显示关键应用。
We present a non-photorealistic algorithm for retargeting large images to small size displays, particularly on mobile devices. This method adapts large images so that important objects in the image are still recognizable when displayed at a lower target resolution. Existing image manipulation techniques such as cropping works well for images containing a single important object, and down-sampling works well for images containing low frequency information. However, when these techniques are automatically applied to images with multiple objects, the image quality degrades and important information may be lost. Our algorithm addresses the case of multiple important objects in an image. The retargeting algorithm segments an image into regions, identifies important regions, removes them, fills the resulting gaps, resizes the remaining image, and re-inserts the important regions. Our approach lies in constructing a topologically constrained epitome of an image based on a visual attention model that is both comprehensible and size varying, making the method suitable for display-critical applications.