Image Congealing via Efficient Feature Selection

Abstract

Congealing for an image ensemble is a joint alignment process to rectify images in the spatial domain such that the aligned images are as similar to each other as possible. Fruitful congealing algorithms were applied to various object classes and medical applications. However, relatively little effort has been taken in the direction of compact and effective feature representations for each image. To remedy this problem, the least-square-based congealing framework is extended by incorporating an unsupervised feature selection algorithm, which substantially removes feature redundancy and leads to a more efficient congealing with even higher accuracy. Furthermore, our novel feature selection algorithm itself is an independent contribution. It is not explicitly linked to the congealing algorithm and can be directly applied to other learning tasks. Extensive experiments are conducted for both the feature selection and congealing algorithms.