Learning to classify human object sketches
Mathias Eitz, James H. Hays · 2011
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.