Modeling Images With Multiple Trace Transforms for Pattern Analysis

Taking advantage of the various available trace transforms generated from a single image, the multiple trace feature (MTF) is proposed as a new image representation. In the process of MTF construction, genetic algorithms (GAs) play a key role as an information fusion tool. The systematic evaluations on a combo face data set comprising ORL, Yale, and UMIST databases reveal that MTF presents high discriminative power in terms of outperforming features extracted from principal component analysis (PCA) and linear discriminant analysis (LDA). In addition, the proposed Bagging-based extension of fitness guides GAs achieving more fitting features for classification.