In 3D object detection and recognition, the object of interest in an image is subject to changes in view-point as well as illumination. It is benefit for the detection and recognition if a representation can be derived to account for view and illumination changes in an effective and meaningful way. In this paper, we propose a method for learning such a representation from a set of un-labeled images containing the appearances of the object viewed from various poses and in various illuminations. Topographic Independent Component Analysis (TICA) is applied for the unsupervised learning to produce an emergent result, that is a topographic map of basis components. The map is topographic in the following sense: the basis components as the units of the map are ordered in the 2D map such that components of similar viewing angle are group in one axis and changes in illumination are accounted for in the other axis. This provides a meaningful set of basis vectors that may be used to construct view subspaces for appearance based multi-view object detection and recognition.

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.