Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection

Abstract

We propose a novel approach to both learning and detecting local contour-based representations for mid-level features. Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images. Patches of human generated contours are clustered to form sketch token classes and a random forest classifier is used for efficient detection in novel images. We demonstrate our approach on both top-down and bottom-up tasks. We show state-of-the-art results on the top-down task of contour detection while being over 200x faster than competing methods. We also achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA and PASCAL, respectively. These gains are due to the complementary information provided by sketch tokens to low-level features such as gradient histograms.

DOI: 10.1109/CVPR.2013.406
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@article{Lim2013SketchTA, title={Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection}, author={Joseph J. Lim and C. Lawrence Zitnick and Piotr Doll{\'a}r}, journal={2013 IEEE Conference on Computer Vision and Pattern Recognition}, year={2013}, pages={3158-3165} }