The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Bag-of-visual-words and spatial extensions for land-use classification
Abstract
We investigate bag-of-visual-words (BOVW) approaches to land-use classification in high-resolution overhead imagery. We consider a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence. We also consider two spatial extensions, the established spatial pyramid match kernel which considers the absolute spatial arrangement of the image features, as well as a novel method which we term the spatial co-occurrence kernel that considers the relative arrangement. These extensions are motivated by the importance of spatial structure in geographic data.
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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
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
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
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Video 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
PCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237
Research works printed but not linked: 13
- 10.1109/34.993558 · DOI · Paper page
- 10.1109/34.93808 · DOI · Paper page
- L. J. V. Gool , T. Moons , and D. Ungureanu . Affine/photometric invariants for planar intensity patterns . In European Conference on Computer Vision , 1996 . L. J. V. Gool, T. Moons, and D. Ungureanu. Affine/photometric invariants for planar intensity patterns. In European Conference on Computer Vision, 1996.
- 10.1109/ICCV.2005.239 · DOI · Paper page
- 10.1109/TSMC.1973.4309314 · DOI · Paper page
- 10.1007/978-3-540-24670-1_18 · DOI · Paper page
- S. Lazebnik , C. Schmid , and J. Ponce . Sparse texture representation using affine-invariant neighborhoods . In IEEE International Conference on Computer Vision and Pattern Recognition , 2003 . S. Lazebnik, C. Schmid, and J. Ponce. Sparse texture representation using affine-invariant neighborhoods. In IEEE International Conference on Computer Vision and Pattern Recognition, 2003.
- 10.5555/850924.851523 · doi:10.5555/850924.851523
- B. S. Manjunath , P. Salembier , and T. Sikora , editors . Introduction to MPEG7: Multimedia Content Description Interface . John Wiley & amp; Sons, 2002 . B. S. Manjunath, P. Salembier, and T. Sikora, editors. Introduction to MPEG7: Multimedia Content Description Interface. John Wiley & Sons, 2002.
- 10.1364/AO.43.000210 · DOI
- 10.1109/CVPR.2006.264 · DOI · Paper page
- 10.5555/946247.946751 · doi:10.5555/946247.946751
- 10.2307/143141 · DOI · Paper page
Cited by
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
AID: A benchmark data set for performance evaluation of aerial scene classification
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2685945 · Cited by 2,372
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2918242 · Cited by 1,951
Cheng, G., Han, J., Zhou, P., & Guo, L. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.10.002 · Cited by 1,007
Metadata
- Authors
- Yang, Y., & Newsam, S.
- Venue
- Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
- Publisher
- ACM
- Published
- 2010-11-02
- Pages
- 270-279
- OpenAlex
- W1980038761
- Cited by
- 2,969
- References
- 12
- FWCI
- 15.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Funders
- Division of Information and Intelligent Systems (IIS-0917069)
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