Integral correlograms and probabilistic diffusion for image tagging
We present a framework intended to assist users in the task of tagging pictures with content descriptors. Histogram- or correlogram features of manually indicated regions of interest are extracted from a few training images; probabilistic diffusion over these prototypes is used to analyze further images. Since speed is pivotal in interactive applications, we apply a fast algorithm for computing local correlograms; moreover, our diffusion-based classifier trains almost instantaneously. Experiments with images downloaded from flickr.com indicate that our method achieves good results even when trained with a single image only.