Hierarchical late fusion for concept detection in videos

Abstract

We deal with the issue of combining dozens of classifiers into a better one, for concept detection in videos. We compare three fusion approaches that share a common structure: they all start with a classifier clustering stage, continue with an intra-cluster fusion and end with an inter-cluster fusion. The main difference between them comes from the first stage. The first approach relies on a priori knowledge about the internals of each classifier (low-level descriptors and classification algorithm) to group the set of available classifiers by similarity. The second and third approaches obtain classifier similarity measures directly from their output and group them using agglomerative clustering for the second approach and community detection for the third one. © 2012 Springer-Verlag.