Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions while preserving its visually and semantically important content. However, not all images can be equally processed. This study introduces the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability, in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates the joint learning of meaningful photographic attributes and image content information, which can facilitate the regularization of the complicated retargetability rating problem. To train and analyze this model, we collect a dataset that contains retargetability scores and meaningful image attributes assigned by six expert raters. The experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show the applications of image retargetability in retargeting method selection, retargeting method assessment and generating a photo collage.

中文翻译：

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图像重定向

现实世界的应用程序可以受益于自动将图像重新定位到不同纵横比和分辨率的能力，同时保留其视觉和语义上的重要内容。但是，并非所有图像都可以进行同等处理。本研究引入了图像重定向的概念，以描述内容感知图像重定向对特定图像的处理程度。我们建议学习一个深度卷积神经网络来对照片可重定向性进行排名，其中照片可重定向性的相对排名直接在损失函数中建模。我们的模型结合了有意义的照片属性和图像内容信息的联合学习，可以促进复杂的可重定向性评级问题的正则化。为了训练和分析这个模型，我们收集了一个数据集，其中包含由六位专家评分者分配的可重定向性分数和有意义的图像属性。实验表明，我们的统一模型可以生成与人类标签高度一致的重定向排名。为了进一步验证我们的模型，我们展示了图像重定向在重定向方法选择、重定向方法评估和生成照片拼贴中的应用。