Abstract: Image-based object recognition systems developed recently don't require the construction of a 3D geometric model, allowing recognition of objects for which current geometric recognition technologies do not apply. Such systems are typically trained with labeled, clean views that cover the whole viewing sphere and can sometimes handle generic, visually similar classes with moderate variation. It has been little explored whether such systems can be trained from imagery that is unlabeled, and whether they can be trained from imagery that is not trivially segmentable.
In this report we investigate how an object recognition system developed previously can be trained from clean images of objects with minimal supervision. After training this system on a single or a small number of views of each object, a simple learning algorithm is able to attract additional views to the object representation, building clusters of views belonging to the same object. We explore how the learning performance improves by extending the set of views, introducing a small amount of supervision, or using more complicated learning algorithms.