Dense interest points

Local features or image patches have become a standard tool in computer vision, with numerous application domains. Roughly speaking, two different types of patch-based image representations can be distinguished: interest points, such as corners or blobs, whose position, scale and shape are computed by a feature detector algorithm, and dense sampling, where patches of fixed size and shape are placed on a regular grid (possibly repeated over multiple scales). Interest points focus on `interesting' locations in the image and include various degrees of viewpoint and illumination invariance, resulting in better repeatability scores. Dense sampling, on the other hand, gives a better coverage of the image, a constant amount of features per image area, and simple spatial relations between features. In this paper, we propose a hybrid scheme, which we call dense interest points, where we start from densely sampled patches yet optimize their position and scale parameters locally. We investigate whether doing so it is possible to get the best of both worlds.