By Topic

Supervised Learning of a Color-Based Active Basis Model for Object Recognition

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
$31 $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)
Bui, T.T.Q. ; Sch. of Mech. Eng., Pusan Nat. Univ., Busan, South Korea ; Keum-Shik Hong

Wu and coworkers introduced an active basis model (ABM) for detecting generic objects in static images. A grey-value local power spectrum was utilized to find a common template and deformable templates from a set of training images and to detect an object in unknown images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short) which includes color information. We adapt the framework of Wu et al. into the learning, detection, and classification of the color-based ABM. However, in order to improve the performance in object recognition, we modify the framework of Wu et al. by using different color-based features in both supervised learning and template matching algorithms. In addition, significant improvements are reported with regard to the proposed color-based ABM for object recognition.

Published in:

Knowledge and Systems Engineering (KSE), 2010 Second International Conference on

Date of Conference:

7-9 Oct. 2010