Object retrieval with large vocabularies and fast spatial matching
- James Philbin, Ondřej Chum, M. Isard, Josef Sivic, Andrew Zisserman
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 17 June 2007
To improve query performance, this work adds an efficient spatial verification stage to re-rank the results returned from the bag-of-words model and shows that this consistently improves search quality, though by less of a margin when the visual vocabulary is large.
Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
- Jiri Matas, Ondřej Chum, Martin Urban, T. Pajdla
- MathematicsBritish Machine Vision Conference
- 2002
The wide-baseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints, is studied and an efficient and practically fast detection algorithm is presented for an affinely-invariant stable subset of extremal regions, the maximally stable extremal region (MSER).
Lost in quantization: Improving particular object retrieval in large scale image databases
- James Philbin, Ondřej Chum, M. Isard, Josef Sivic, Andrew Zisserman
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 1 June 2008
The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of images…
Matching with PROSAC - progressive sample consensus
- Ondřej Chum, Jiri Matas
- Computer ScienceComputer Vision and Pattern Recognition
- 20 June 2005
A new robust matching method, PROSAC, which exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative correspondences and achieves large computational savings.
Fine-Tuning CNN Image Retrieval with No Human Annotation
- Filip Radenović, Giorgos Tolias, Ondřej Chum
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 3 November 2017
It is shown that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval.
Locally Optimized RANSAC
- Ondřej Chum, Jiri Matas, J. Kittler
- Computer ScienceDAGM-Symposium
- 10 September 2003
The locally optimized ransac makes no new assumptions about the data, on the contrary – it makes the above-mentioned assumption valid by applying local optimization to the solution estimated from the random sample.
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
- Ondřej Chum, James Philbin, Josef Sivic, M. Isard, Andrew Zisserman
- Computer ScienceIEEE International Conference on Computer Vision
- 26 December 2007
This paper brings query expansion into the visual domain via two novel contributions: strong spatial constraints between the query image and each result allow us to accurately verify each return, suppressing the false positives which typically ruin text-based query expansion.
Robust wide-baseline stereo from maximally stable extremal regions
- Jiri Matas, Ondřej Chum, Martin Urban, T. Pajdla
- MathematicsImage and Vision Computing
- 1 September 2004
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
- Filip Radenović, Giorgos Tolias, Ondřej Chum
- Computer ScienceEuropean Conference on Computer Vision
- 8 April 2016
This work proposes to fine-tune CNN for image retrieval from a large collection of unordered images in a fully automated manner and shows that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes.
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
- Filip Radenović, Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondřej Chum
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 29 March 2018
Issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets are addressed: in particular, annotation errors, the size of the dataset, and the level of challenge are addressed.
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