Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

Abstract

Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve better consistency with background knowledge. Finally, empirical evaluation on two benchmark datasets show that our approach can significantly increase recall by up to 6.3 points without compromising mean average precision, when compared to the state-of-the-art baseline.

Keywords

Machine Learning, Knowledge-based Learning, Robotics and Vision, Vision and Perception, Artificial intelligence, Deep neural networks, Object recognition, Optimization, Semantics

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence: Melbourne, Australia, August 19-25

First Page

1661

Last Page

1667

ISBN

9780999241103

Identifier

10.24963/ijcai.2017/230

Publisher

International Joint Conferences on Artificial Intelligence

City or Country

Melbourne

Additional URL

https://doi.org/10.24963/ijcai.2017/230

Download

Share

COinS