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How do humans sketch objects?

ACM Transactions on Graphics · 2012 · Vol. 31(4) · pp. 1–10
Mathias EitzJames HaysMarc Alexa

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.

Advanced Image and Video Retrieval TechniquesVisual Attention and Saliency DetectionVideo Analysis and SummarizationSketchComputer scienceSketch recognitionRendering (computer graphics)Artificial intelligenceRepresentation (politics)Object (grammar)PerceptionGesture

Funding

  • National Science Foundation
Citations
824
FWCI
32.59
field-weighted impact
References
40
Percentile
100%
vs. same field & year
Citations per year
References
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision · 2009 · 19,127 citations
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision · 2007 · 4,112 citations
Image retrieval
ACM Computing Surveys · 2008 · 2,995 citations
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision · 2004 · 54,768 citations
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