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 data-driven 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 high-quality visual objects that can help with various data-driven image applications. Our method is based on dense proposal generation and objectness-based re-ranking. A novel deep convolutional neural network is designed for the inference of proposal objectness, the probability of a proposal containing optimally-located 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 re-ranked according to the output of our network generally achieve higher performance than those produced by other state-of-the-art 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 data-driven image applications.

本研究聚焦于建立从互联网图像收集中得出视觉对象数据库的方法学，并利用稠密 Proposal 生成和基于物体性的重排序方法，通过深度卷积神经网络设计出的前景完整性和充实度相互补充的计量方式，提高了物体建议的排序性能，并利用该方法成功构建了包含超过1.2百万个视觉对象的数据库，实现了不少于多个数据驱动图像应用。

基于深度学习的目标显著性评估从互联网图像中收集视觉对象