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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper introduces a system that creates a classifier at test time for a given test image using a set of training examples in a specific neighborhood of the test image. The authors method proposes how to identify the neighborhood with the most informative training examples for the given test image. The authors indicate that their method is well suited to fine-grained image classification tasks. The authors' key observation is that while training images that are nearest to the test image may be informative, the most informative set of training images must be found by estimating the informativeness of all members of a neighborhood set together. The main contribution of this paper is a method for estimating the most informative neighborhood set in an online fashion for each new test image.