View-based clustering of object appearances based on independent subspace analysis
In 3D object detection and recognition, an object of interest is subject to changes in view as well as in illumination and shape. For image classification purpose, it is desirable to derive a representation in which intrinsic characteristics of the object are captured in a low dimensional space while effects due to artifacts are reduced. In this paper, we propose a method for view-based unsupervised learning of object appearances. First, view-subspaces are learned from a view-unlabeled data set of multi-view appearances, using independent subspace analysis (ISA). A learned view-subspace provides a representation of appearances at that view, regardless of illumination effect. A measure, called view-subspace activity, is calculated thereby to provide a metric for view-based classification. View-based clustering is then performed by using maximum view-subspace activity (MVSA) criterion. This work is to the best of our knowledge the first devoted research on view-based clustering of images.