Efficient Approximate Medoids of Temporal Sequences

In order to compactly represent a set of data, its medoid (the element with minimum summed distance to all other elements) is a useful choice. This has applications in clustering, compression and visualisation of data. In multimedia data, the set of data is often sampled as a sequence in time or space, such as a video shot or views of a scene. The exact calculation of the medoid may be costly, especially if the distance function between elements is not trivial. While approximation methods for medoid selection exist, we show in this work that they do not perform well on sequences of images. We thus propose a novel algorithm for efficiently selecting an approximate medoid of a temporal sequence and assess its performance on two large-scale video data sets.