Vehicle Recognition with Constrained Multiple Instance SVMs

Abstract: Vehicle recognition is a challenging task with many useful applications. State-of-the-art methods usually learn discriminative classifiers for different vehicle categories or different viewpoint angles, but little work has explored vehicle recognition using semantic visual attributes. In this paper, we propose a novel iterative multiple instance learning method to model local attributes and viewpoint angles together in the same framework. We expand the standard MI-SVM formulation to incorporate pairwise constraints based on viewpoint relations within positive exemplars. We show that our method is able to generate discriminative and semantic local attributes for vehicle categories. We also show that we can estimate viewpoint labels more accurately than baselines when these annotations are not available in the training set. We test the technique on the Stanford cars and INRIA vehicles datasets, and compare with other methods.