Hypergraph Propagation and Community Selection for Objects RetrievalDownload PDF

May 21, 2021 (edited Jan 28, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Image search, Landmark retrieval, Uncertainty, Diffusion, Query expansion, Matching, Hypergraph
  • TL;DR: We propose a hypergraph model to settle the ambiguity problem of propagation, and we use the graph information to reduce the spatial verification cost.
  • Abstract: Spatial verification is a crucial technique for particular object retrieval. It utilizes spatial information for the accurate detection of true positive images. However, existing query expansion and diffusion methods cannot efficiently propagate the spatial information in an ordinary graph with scalar edge weights, resulting in low recall or precision. To tackle these problems, we propose a novel hypergraph-based framework that efficiently propagates spatial information in query time and retrieves an object in the database accurately. Additionally, we propose using the image graph's structure information through community selection technique, to measure the accuracy of the initial search result and to provide correct starting points for hypergraph propagation without heavy spatial verification computations. Experiment results on ROxford and RParis show that our method significantly outperforms the existing query expansion and diffusion methods.
  • Supplementary Material: pdf
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
  • Code: https://sgvr.kaist.ac.kr/~guoyuan/hypergraph_propagation/
10 Replies

Loading