Multistrategical approach in visual learning

Abstract

In this paper, we propose a novel visual learning framework to develop flexible and accurate object recognition methods. Currently, most of visual learning based recognition methods adopt the monostrategy learning framework using a single feature. However, the real-world objects are so complex that it is quite difficult for monostrategy method to correctly classify them. Thus, utilizing a wide variety of features is required to precisely distinguish them. In order to utilize various features, we propose multistrategical visual learning by integrating multiple visual learners. In our method, multiple visual learners are collaboratively trained. Specifically, a visual learner L intensively learns the examples misclassified by the other visual learners. Instead, the other visual learners learn the examples misclassified by L. As a result, a powerful object recognition method can be developed by integrating various visual learners even if they have mediocre recognition performance. © Springer-Verlag Berlin Heidelberg 2007.