Accuracy versus speed in context-based object detection

doi:10.1016/j.patrec.2006.08.004

Accuracy versus speed in context-based object detection

2007-04-01

0

PRE

AI

DOI：10.1016/j.patrec.2006.08.004

原文链接

原文求助

摘要

En 中文

The visual detection and recognition of objects is facilitated by context. This paper studies two types of learning methods for realizing context-based object detection in paintings. The first method is called the gradient method; it learns to transform the spatial context into a gradient towards the object. The second method, the context-detection method, learns to detect image regions that are likely to contain objects. The accuracy and speed of both methods are evaluated on a face-detection task involving natural and painted faces in a wide variety of contexts. The experimental results show that the gradient method enhances accuracy at the cost of computational speed, whereas the context-detection method optimises speed at the cost of accuracy. The different results of both methods are argued to arise from the different ways in which the methods trade-off accuracy and speed. We conclude that both the gradient method and the context-detection method can be applied to reliable and fast object detection in paintings and that the choice for either method depends on the application and user constraints. (c) 2006 Elsevier B.V. All rights reserved.

Keyword:

computer vision

object detection

face detection

machine learning

Accuracy versus speed in context-based object detection

摘要

期刊

机构

引用论文