Local Feature Based Unsupervised Alignment of Object Class Images

Alignment of objects is a predominant problem in visual object categorisation (VOC). State-of-the-art part-based VOC methods try to automatically learn object parts and their spatial variation, which is difficult for objects in arbitrary poses. A straightforward solution is to annotate images with a set of “object landmarks”, but due to laborious work required, less supervised methods are preferred. Effective semi-supervised VOC methods have been introduced, but none of them explicitly define an alignment procedure or study its effect to overall VOC performance. Unsupervised alignment has been recognised as its own problem referred to as “spatial image congealing” and a number of congealing methods have been proposed. These methods are mainly seminal work to Learned-Miller [3, 4] extending and improving the original algorithm. The main drawback of the congealing methods is that they are iterative optimisation methods operating on pixel-level and thus require at least moderate initial alignment to converge. Our approach [2] deviates from the congealing works by the fact that we utilise local features instead of pixel level processing, i.e. featurebased congealing. Our solution is more similar to those used in the partbased VOC methods, but we explicitly define the alignment algorithm and measure its performance.