Feature clustering with fading affect bias: building visual vocabularies on the fly

Abstract

We present a fast and accurate center-based, single-pass approach for data clustering in a non-parametric fashion, with main focus on features from large image collections and streaming videos. We use a dictionary of clusters and a list (‘short memory’) of centers temporarily stored during parsing the data. The latter is used to determine emerging clusters, not previously observed, or outliers that are discarded. Our method assigns features to existing or newly created clusters with constant-time computations, and it can be used for more general static datasets or sequential (streaming) data. In our experiments, we make extensive comparisons with approaches commonly used in feature clustering, with respect to accuracy and efficiency.