Pattern recognition with local invariant features
Local invariant features have shown to be very successful for recognition. They are robust to occlusion and clutter, distinctive as well as invariant to image transformations. In this chapter recent progress on local invariant features is summarized. It is explained how to extract scale and affine-invariant regions and how to obtain discriminant descriptors for these regions. It is then demonstrated that combining local features with pattern classification techniques allows for texture and category-level object recognition in the presence of varying viewpoints and background clutter.