Deep Feature Guided Image Retargeting Image retargeting is the technique to display images via devices with various aspect ratios and sizes. Traditional contentaware retargeting methods rely on lowlevel features to predict pixelwise importance and can hardly preserve both the structure lines and salient regions of the source image. To address this problem, we propose a novel adaptive image warping approach which integrates with deep convolutional neural network. In the proposed method, a visual importance map and a foreground mask map are generated by a pretrained network. The two maps and other constraints guide the warping process to yield retargeted results with less distortions. Extensive experiments in terms of visual quality and a user study are carried out on the widely used RetargetMe dataset. Experimental results show that our method outperforms current stateofart image retargeting methods.

Harvesting Visual Objects from Internet Images via Deep Learning Based Objectness Assessment. The collection of internet images has been growing in an astonishing speed. It is undoubted that these images contain rich visual information that can be useful in many applications, such as visual media creation and datadriven image synthesis. In this paper, we focus on the methodologies for building a visual object database from a collection of internet images. Such database is built to contain a large number of highquality visual objects that can help with various datadriven image applications. Our method is based on dense proposal generation and objectnessbased reranking. A novel deep convolutional neural network is designed for the inference of proposal objectness, the probability of a proposal containing optimallylocated foreground object. In our work, the objectness is quantitatively measured in regard of completeness and fullness, reflecting two complementary features of an optimal proposal: a complete foreground and relatively small background. Our experiments indicate that object proposals reranked according to the output of our network generally achieve higher performance than those produced by other stateoftheart methods. As a concrete example, a database of over 1.2 million visual objects has been built using the proposed method, and has been successfully used in various datadriven image applications.