# Harvesting Visual Objects from Internet Images via Deep Learning Based Objectness Assessment

## TL;DR

This paper introduces a deep learning approach for assessing objectness in internet images, enabling the construction of a large, high-quality visual object database for various image applications.

## Contribution

A novel deep convolutional neural network for objectness inference that improves proposal ranking and database quality over existing methods.

## Key findings

- Re-ranked proposals outperform state-of-the-art methods.
- Constructed a database of over 1.2 million visual objects.
- Enhanced performance in data-driven image applications.

## Abstract

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.