SymCity: feature selection by symmetry for large scale image retrieval
Proceedings of the 20th ACM international conference on Multimedia
Approximate gaussian mixtures for large scale vocabularies
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Locality preserving verification for image search
Proceedings of the 21st ACM international conference on Multimedia
World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging
Proceedings of the 5th ACM Multimedia Systems Conference
Generative Methods for Long-Term Place Recognition in Dynamic Scenes
International Journal of Computer Vision
Hough Pyramid Matching: Speeded-Up Geometry Re-ranking for Large Scale Image Retrieval
International Journal of Computer Vision
Towards large-scale geometry indexing by feature selection
Computer Vision and Image Understanding
| Hi-index | 0.00 |
A wide range of properties and assumptions determine the most appropriate spatial matching model for an application, e.g. recognition, detection, registration, or large scale image retrieval. Most notably, these include discriminative power, geometric invariance, rigidity constraints, mapping constraints, assumptions made on the underlying features or descriptors and, of course, computational complexity. Having image retrieval in mind, we present a very simple model inspired by Hough voting in the transformation space, where votes arise from single feature correspondences. A relaxed matching process allows for multiple matching surfaces or non-rigid objects under one-to-one mapping, yet is linear in the number of correspondences. We apply it to geometry re-ranking in a search engine, yielding superior performance with the same space requirements but a dramatic speed-up compared to the state of the art.