  Latent feature models are attractive for image modeling, since images
generally contain multiple objects. However, many latent feature models ignore
that objects can appear at different locations or require pre-segmentation of
images. While the transformed Indian buffet process (tIBP) provides a method
for modeling transformation-invariant features in unsegmented binary images,
its current form is inappropriate for real images because of its computational
cost and modeling assumptions. We combine the tIBP with likelihoods appropriate
for real images and develop an efficient inference, using the cross-correlation
between images and features, that is theoretically and empirically faster than
existing inference techniques. Our method discovers reasonable components and
achieve effective image reconstruction in natural images.
