Learning to classify human object sketches.
SIGGRAPH '11: Special Interest Group on Computer Graphics and Interactive Techniques Conference Vancouver British Columbia Canada August, 2011(2011)
摘要
We present ongoing work on object category recognition from binary human outline sketches. We first define a novel set of 187 "sketchable" object categories by extracting the labels of the most frequent objects in the LabelMe dataset. In a large-scale experiment, we then gather a dataset of over 5,500 human sketches, evenly distributed over all categories. We show that by training multi-class support vector machines on this dataset, we can classify novel sketches with high accuracy. We demonstrate this in an inter-active sketching application that progressively updates its category prediction as users add more strokes to a sketch.
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