Learning 3D object recognition from an unlabelled and unordered training set

Abstract

This paper proposes an unsupervised learning technique for object recognition from an unlabelled and unordered set of training images. It enables the robust recognition of complex 3D objects in cluttered scenes, under scale changes and partial occlusion. The technique uses a matching based on the consistency of two different descriptors characterising the appearance and shape of local features. The variation of each local feature with viewing direction is modeled by a multi-view feature model. These multi-view feature models can be matched directly to the features found in a test image. This avoids a matching to all training views as necessary for approaches based on canonical views. The proposed approach is tested with real world objects and compared to a supervised approach using features characterised by SIFT descriptors (Scale Invariant Feature Transform). These experiments show that the performance of our unsupervised technique is equal to that of a supervised SIFT object recognition approach. © Springer-Verlag Berlin Heidelberg 2007.