CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor董未名
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword图像缩放 图像可缩放度 卷积神经网络 二值属性 相对属性
Other Abstract


研究图像本身对内容相关缩放操作的适应程度。从图像高层属性分析角度定义了“可缩放度”,并分析“可缩放度”与其他视觉属性的关联关系。为了学习和分析可缩放度,本文构建了一个涵盖类别更广、数量更多、限制更少的基准图像库(Retargeting and Attributes DatabaseRADB),该图像库包含专家定义的视觉属性信息和专家评估的每个图像的可缩放分数。





Content-aware image retargeting (CAIR) addresses the increasing demand to display image contents on devices of different resolutions and aspect ratios while preserving its visually important content and avoiding noticeable artifacts. As a result,content-aware image retargeting (CAIR) is widely used in graphic design, photo websites browsing, online-video cover making and so on. Even if the state-of-the-art image retargeting techniques can successfully handle more and more images, it is often not clear beforehand whether a specific image would be successfully retargeted.


In this dissertation, we firstly introduce the notion of image retargetability as a high-level attribute to describes how well a particular image can be handed by content-aware image retargeting(CAIR). Furthermore, we propose a method to learn and predict the retargetability, taking advantage of convolutional neural network and related visual attributes. To further validate our model,we show applications of image retargetability. The issues and contributions being addressed include:


We study the adaptation of the image itself to content-aware image retargeting(CAIR).We proposed retargetability as a novel image property and developed a computational predictor for it. At the same time, we analyze the correlation between retargetability and attributes.To learn and predict retargetability, we have assembled a new retargeting and attributes database (RADB) which contains retargetability scores and meaningful attributes assigned to each image by an expert rater.

We build and modify the learning model of retargetability based on convolutional neural network. As to the automatic retargetability scoring, we demonstrate the multi-task learning approach by jointly learning visual attributes from deep features and feature sharing for retargetability. This approach allows the model to share information among different visual attributes and by making use of the shared feature, the model mines and predicts retargetability  from an image.

We design applications related with retargetability. We propose the strategy to choose the "best" image retargeting algorithm without result-images. We show how to utilize image retargetability in applications of retargeting method assessment, photo collage and photo album cover generation. And we propose the method to optimize the layout of image material in the graphic design.

Document Type学位论文
Recommended Citation
GB/T 7714
孟一平. 图像可缩放度的研究与应用[D]. 北京. 中国科学院研究生院,2017.
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