Automatic Thumbnail Generation Based on Visual Representativeness and Foreground Recognizability

Jingwei Huang, Huarong Chen, Bin Wang, Stephen Lin
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引用次数: 20

Abstract

We present an automatic thumbnail generation technique based on two essential considerations: how well they visually represent the original photograph, and how well the foreground can be recognized after the cropping and downsizing steps of thumbnailing. These factors, while important for the image indexing purpose of thumbnails, have largely been ignored in previous methods, which instead are designed to highlight salient content while disregarding the effects of downsizing. We propose a set of image features for modeling these two considerations of thumbnails, and learn how to balance their relative effects on thumbnail generation through training on image pairs composed of photographs and their corresponding thumbnails created by an expert photographer. Experiments show the effectiveness of this approach on a variety of images, as well as its advantages over related techniques.
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基于视觉代表性和前景可识别性的缩略图自动生成
我们提出了一种基于两个基本考虑的自动缩略图生成技术:它们在视觉上代表原始照片的程度,以及缩略图裁剪和缩小步骤后前景的识别程度。这些因素虽然对缩略图的图像索引目的很重要,但在以前的方法中很大程度上被忽略了,这些方法的目的是突出突出的内容,而忽略缩小尺寸的影响。我们提出了一组图像特征来对缩略图的这两种考虑进行建模,并通过训练由照片和专业摄影师创建的相应缩略图组成的图像对来学习如何平衡它们对缩略图生成的相对影响。实验证明了该方法在多种图像上的有效性,以及相对于相关技术的优势。
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