Learning complex image patterns with Scale and Shift Invariant Sparse Coding

The image patches learned by recent works are usually only bar-like or Gabor-like patterns. However those simple patterns are not meaningful enough to capture higher level information. In this study, we try to learn more complex image patterns from unaligned images. We propose Scale And Shift Invariant Sparse Coding (SASISC), which aligns basis patches at proper locations and scales to reconstruct the whole image. The experiment results on unaligned images show that SASISC can explain the images much better than the original sparse coding, and can extract more complex image patterns.