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Gradient-descent methods have exhibited fast and reliable performance forimage alignment in the facial domain, but have largely been ignored by thebroader vision community. They require the image function be smooth and(numerically) differentiable -- properties that hold for pixel-basedrepresentations obeying natural image statistics, but not for more generalclasses of non-linear feature transforms. We show that transforms such as DenseSIFT can be incorporated into a Lucas Kanade alignment framework by predictingdescent directions via regression. This enables robust matching of instancesfrom general object categories whilst maintaining desirable properties of LucasKanade such as the capacity to handle high-dimensional warp parametrizationsand a fast rate of convergence. We present alignment results on a number ofobjects from ImageNet, and an extension of the method to unsupervised jointalignment of objects from a corpus of images.

Regression-Based Image Alignment for General Object Categories