  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.
