The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
How do humans sketch objects?
Abstract
Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as 'teapot' or 'car'. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.
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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
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The pascal visual object classes (VOC) challenge
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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
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
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
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SUN database: Large-scale scene recognition from abbey to zoo
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Self-citationImage retrieval: Ideas, influences, and trends of the new age
Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008) · ACM Computing Surveys · doi:10.1145/1348246.1348248 · Cited by 3,040
Cited by
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Edge Boxes: Locating Object Proposals from Edges
Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656
The sketchy database: learning to retrieve badly drawn bunnies
Sangkloy, P., Burnell, N., Ham, C., & Hays, J. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925954 · Cited by 622
Self-citationEdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
Nazeri, K., Ng, E., Joseph, T., Qureshi, F. Z., & Ebrahimi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00212 · Cited by 538
SpotTune: Transfer Learning Through Adaptive Fine-Tuning
Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., & Feris, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00494 · Cited by 506
Metadata
- Authors
- Eitz, M., Hays, J., & Alexa, M.
- Venue
- ACM Transactions on Graphics
- Published
- 2012-07
- Volume
- 31
- Issue
- 4
- Pages
- 1-10
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W1972420097
- Cited by
- 868
- References
- 34
- FWCI
- 36.54
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
