Several re-ranking algorithms have been proposed recently. Some effective approaches are based on complex graph-based diffusion processes, which usually are time consuming and therefore inappro- priate for real-world large scale shape collections.
In this article, the authors present a novel rank-based algorithm for improving the effectiveness of shape retrieval tasks. The algorithm models each ranked list as a graph, establishing similarity connections among all top-k images. Next, a graph fusion approach is employed for obtaining a single graph representing the whole collection and exploiting the relationships encoded in the dataset manifold. Based on the fused graph, a new distance is learned and a new set of ranked lists is computed. The effectiveness of the proposed approach is demonstrated by the performance of an extensive experimental protocol considering widely used shape collections.
Daniel Carlos Guimarães Pedronette, Jurandy Almeida, Ricardo da S. Torres, A graph-based ranked-list model for unsupervised distance learning on shape retrieval, Pattern Recognition Letters, Volume 83, Part 3, 1 November 2016, Pages 357-367, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2016.05.021.