Object Recognition with Informative Features and Linear Classification

In this paper we show that efficient object recognition can be obtained by combining informative features with linear classification. The results demonstrate the superiority of informative class-specific features, as compared with generic type features such as wavelets, for the task of object recognition. We show that information rich features can reach optimal performance with simple linear separation rules, while generic feature based classifiers require more complex classification schemes. This is significant because efficient and optimal methods have been developed for spaces that allow linear separation. To compare different strategies for feature extraction, we trained and compared classifiers working in feature spaces of the same low dimensionality, using two feature types (image fragments vs. wavelets) and two classification rules (linear hyperplane and a Bayesian Network). The results show that by maximizing the individual information of the features, it is possible to obtain efficient classification by a simple linear separating rule, as well as more efficient learning.