We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. In experiments we evaluate different sizes and types of components and three standard techniques for component selection. The component classifiers are finally compared to global classifiers on a database of four objects.

We present a component-based approach for recognizing objectsunder large pose changes. From a set of training images of a givenobject we extract a large number of components which are clusteredbased on the similarity of their image features and their locations withinthe object image. The cluster centers build an initial set of componenttemplates from which we select a subset for the final recognizer.In experiments we evaluate different sizes and types of components andthree standard techniques for component selection. The component classifiersare finally compared to global classifiers on a database of fourobjects.