The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Total recall II: Query expansion revisited
Abstract
Most effective particular object and image retrieval approaches are based on the bag-of-words (BoW) model. All state-of-the-art retrieval results have been achieved by methods that include a query expansion that brings a significant boost in performance. We introduce three extensions to automatic query expansion: (i) a method capable of preventing tf-idf failure caused by the presence of sets of correlated features (confusers), (ii) an improved spatial verification and re-ranking step that incrementally builds a statistical model of the query object and (iii) we learn relevant spatial context to boost retrieval performance. The three improvements of query expansion were evaluated on standard Paris and Oxford datasets according to a standard protocol, and state-of-the-art results were achieved.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 1×
“Virtually all aspects of particular object BoW-type retrieval have been intensively studied: feature detectors and descriptors [14, 5, 25, 15, 16], vocabulary construction [23, 18, 21, 9, 17], spatial verification and re-ranking [21, 9], document metric learning [12, 10, 6] and dimensionality reduction [11, 20].”
BackgroundVideo Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
Self-citationHamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901 · cited 2×
“In this paper, the approach of approximate k-means with 1-million visual words was chosen from the many different vocabulary construction methods, such as [18, 21, 9].”
BackgroundMethodFAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472
Cited by
Deep Image Retrieval: Learning Global Representations for Image Search
Gordo, A., Almazán, J., Revaud, J., & Larlus, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_15 · Cited by 787
Particular object retrieval with integral max-pooling of CNN activations
Tolias, G., Sicre, R., & Jégou, H. (2015) · arXiv · doi:10.48550/arxiv.1511.05879 · Cited by 744
Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
Weyand, T., Araujo, A., Cao, B., & Sim, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00265 · Cited by 303
Metadata
- Authors
- Chum, O., Mikulik, A., Perdoch, M., & Matas, J.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 889-896
- OpenAlex
- W2023991840
- Cited by
- 287
- References
- 7
- FWCI
- 19.37
- Type
- conference paper
- Language
- en
- Open access
- closed
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