The problem of online sampling of data, can be seen as a
generalization of the classical secretary problem. The goal
is to maximize the probability of picking the k highest scoring
samples in our data, making the decision to select or reject
a sample on-line. We present a new and simple on-line
algorithm to optimally make this selection. We then apply
this algorithm to a sequence of images taken by a mobile
robot, with the goal of identifying the most interesting and
informative images.