Joint Feature Selection for Object Detection and Recognition
Jerod J. Weinman, Allen R. Hanson, Erik Learned-Miller · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2006
Classifiers for object categorization and identification, such as face detectors and face recognizers, are often trained separately and operated in a feed-forward fashion. Selecting a small number of features for these tasks is important to prevent over-fitting and reduce computation. However, when a system has such related or sequential tasks, training and selecting features for these tasks independently may not be optimal. We propose a framework for choosing features to be shared between categorization and identification tasks. The result is a system that achieves better performance with a given number of features. We demonstrate with experiments using text and car detection as categorization tasks, and character and vehicle type recognition as identification tasks. 1.