CEA LIST's participation at MediaEval 2013 retrieving diverse social images task
Résumé
Clustering is by far the most popular diversification technique described in literature. Its aim is to group together images that are related following some similarity criterion. Here we aim to tackle the problem differently and explore a reranking-based techniques that increase diversity by considering the "informativeness" of each new image with respect to the set of images that were already selected. "Informativeness" is defined using social cues, such as user ID and date, visual cues extracted from the low-level representation of the image or multimedia cues that combine visual and textual processing. For some of the runs, we also exploit an initial k Nearest Neighbors (k-NN) inspired image reranking that is meant to reduce the amount of noise present in the result set.