In this article, we are interested in accelerating similarity search in collections of image contents described in high dimensional spaces. The presented framework, called HiPeR (for Hierarchical Progressive Retrieval), is based on  a   hierarchy  of subspaces and indexes: it performs similarity search across feature spaces of different dimensions, by beginning with the lowest dimensions up to the highest ones, with the aim of minimizing the effects of curse of dimensionality. HiPeR significantly accelerates exact retrieval even with the best indexes, and also enables progressive retrieval, i.e. the possibility to provide results to the user progressively, with refinements until userpsilas satisfaction.
['Nouha Bouteldja', 'Valérie Gouet-Brunet']
Exact and progressive image retrieval with the HiPeR framework