| Due to recent advances in the art, object recognition may soon replace low-level feature extraction processes in automatic image database annotation. However, improvement in performance is still an important consideration. In addition, model acquisition for appearance-based object recognition is tedious, since such systems usually require training on a large set of segmentable example views that cover variation among class exemplars. These views have to be labeled with object identity and pose.; In this thesis we first develop and analyze a feature-based object recognition system that demonstrates good recognition of a variety of 3D shapes, with full orthographic invariance. We report the results of large-scale tests that evaluate recognition performance in conditions of background clutter and partial occlusion, as well as generic capabilities of the system. We develop a statistical framework for predicting the performance in a variety of situations from a few basic measurements. We investigate the performance of object recognition systems, to see which, if any, design axes of such systems hold the greatest potential for improving performance. One conclusion is that the greatest leverage seems to lie at the level of intermediate feature construction. We also analyze the effect of other improvements, such as parallelization and the use of multiple views.; We then formalize a system for constructing 3D recognition models using large, cluttered visual corpora, in a minimally supervised manner. After giving it a few seed pictures of an object class (say a couple of pictures of cars), the system is given access to an unlabeled image database containing, among other images, other pictures of the object. The system then explores the image database, augmenting its representation of the object (in this case the car) class to include new information whenever it finds a near enough match to the existing representation. After exposure to sufficient imagery, the system will usually have a general model of the car that can label cars in the entire database and other databases. We obtain a significant improvement in recognition performance when training the system from unlabeled cluttered background images, as opposed to training only on the labeled, black background seed image. The approach could use any appearance-based 3D object recognition system. |
