AdaBoost Gabor Feature Selection for Classification
Linlin Shen, Bai Li · 2004
This paper describes an application of the AdaBoost algorithm for selecting Gabor features for classification. Gabor wavelets are powerful image descriptors but they often result in very high dimensional feature vectors, which rend them impractical for real applications. We have trained a classifier using the AdaBoost algorithm with a set of Gabor features extracted from images. Compared with the huge number of features used by typical classifiers using Gabor features, our classifier selects only about one hundred features. Whilst significant memory and computation cost has been saved, our classifier still achieves very high classification accuracy. Two image datasets have been used to test our system, and only 20 features are required to achieve zero error rates on the car image dataset.