The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors
Abstract
We introduce a benchmark for evaluating the performance of large-scale sketch-based image retrieval systems. The necessary data are acquired in a controlled user study where subjects rate how well given sketch/image pairs match. We suggest how to use the data for evaluating the performance of sketch-based image retrieval systems. The benchmark data as well as the large image database are made publicly available for further studies of this type. Furthermore, we develop new descriptors based on the bag-of-features approach and use the benchmark to demonstrate that they significantly outperform other descriptors in the literature.
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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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
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
Hamming 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 1×
“While the use of local descriptors is a common approach in example based image retrieval [4], [5], [6], [7], SBIR systems up to now still employ global descriptors and thus inherit their drawbacks, mainly being not invariant to affine transformations.”
MethodSampling Strategies for Bag-of-Features Image Classification
Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927
Cited by
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
Castrejon, L., Aytar, Y., Vondrick, C., Pirsiavash, H., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.321 · Cited by 188
When is a Tool a Tool? User Perceptions of System Agency in Human–AI Co-Creative Drawing
Lawton, T., Grace, K., & Ibarrola, F. J. (2023) · Proceedings of the 2023 ACM Designing Interactive Systems Conference · doi:10.1145/3563657.3595977 · Cited by 72
Metadata
- Authors
- Eitz, M., Hildebrand, K., Boubekeur, T., & Alexa, M.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2011-11
- Volume
- 17
- Issue
- 11
- Pages
- 1624-1636
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2153404544
- Cited by
- 413
- References
- 7
- FWCI
- 14.74
- Type
- journal article
- Language
- en
- Open access
- closed
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