  This paper introduces a family of local feature aggregation functions and a
novel method to estimate their parameters, such that they generate optimal
representations for classification (or any task that can be expressed as a cost
function minimization problem). To achieve that, we compose the local feature
aggregation function with the classifier cost function and we backpropagate the
gradient of this cost function in order to update the local feature aggregation
function parameters. Experiments on synthetic datasets indicate that our method
discovers parameters that model the class-relevant information in addition to
the local feature space. Further experiments on a variety of motion and visual
descriptors, both on image and video datasets, show that our method outperforms
other state-of-the-art local feature aggregation functions, such as Bag of
Words, Fisher Vectors and VLAD, by a large margin.
