By Topic

Method to improve the performance of the AdaBoost algorithm using Gaussian probability distribution

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

3 Author(s)
Jeong-Hyun Kim ; Dept. of Intell. Machinery Eng., Pusan Nat. Univ., Pusan ; Jong-Hyun Park ; Dong-Joong Kang

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper propose the probability AdaBoost Algorithm that is made the Gaussian probability distribution of feature value and evaluate the probability value as how to close the mean of the Gaussian probability distribution. In the learning procedure, the weak classifier is selected by the evaluation that is how positive distribution to become independent negative distribution and how positive distribution to close. The weight is updated to exponential dasia0psila or dasia1psila in conventional AdaBoost but the proposal method is updated to exponential the real value between dasia0psila and dasia1psila by the Gaussian distribution. Hence, the selection of weak classifier is reflected more preciously to weight update. It is no specific threshold to study the proposed method using the Gaussian probability distribution of positive feature value. It is learned by 2 distribution of positive and negative date; therefore, The modeling for to classify the positive is more natural. and we prove more previously detection in experiment.

Published in:

Control, Automation and Systems, 2008. ICCAS 2008. International Conference on

Date of Conference:

14-17 Oct. 2008