Component based recognition of objects in an office environment

Abstract

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.