“Learning to classify human object sketches” by Eitz and Hays

  • ©Mathias Eitz and James Hays




    Learning to classify human object sketches



    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.


    1. Eitz, M., Hildebrand, K., Boubekeur, T., and Alexa, M. 2011. Sketch-based image retrieval: benchmark and bag-offeatures descriptors. IEEE Trans. Vis. Comp. Graph.. Preprints.
    2. Sivic, J., and Zisserman, A. 2003. Video Google: A Text Retrieval Approach to Object Matching in Videos. In IEEE ICCV.

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