The Evaluation of Wavelet and Data Driven Feature Selection for Image Understanding

Image mining with pattern recognition methods has been widely used to understand the image knowledge. The general rule for the number of features is that the number can not be too much to improve the efficiency and speed of mining. This paper presents the wavelet methods for feature compression, and gives the evaluation of the wavelet methods comparing with data driven feature selection using the example of the project of image mining of pathogen yeast Cryptococcus Neoformans. The experiments show that the wavelet methods for feature compression are almost as effective as data driven feature selection to identify variance pathogen condition. The experiments are built on the training images set and evaluated using the new test set images with machine learning tool WEKA.