By Topic

Visual learning framework based on reinforcement learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Fang Liu ; Dept. of Autom., Shanghai Jiao Tong Univ., China ; Jianbo Su

This paper proposes a novel visual learning framework for attention control in active computer vision. The general hierarchical framework is constructed by using reinforcement learning to organize the image processing procedures and find optimal control strategy so as to efficiently reduce the computational cost. This framework allows the interactions between information in different levels and integration of visual modules with other machine learning algorithms, which make it possible to fulfill the specific task quickly by only processing relatively small quantities of data. The experiments of the selective attention on robot are provided to verify the effectiveness of the proposed framework.

Published in:

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on  (Volume:6 )

Date of Conference:

15-19 June 2004