Fast, Accurate Detection of 100,000 Object Classes on a Single Machine

CVPR 2013 Thomas DeanMark A. RuzonMark SegalJonathon ShlensSudheendra VijayanarasimhanJay Yagnik

Many object detection systems are constrained by the time required to convolve a target image with a bank of filters that code for different aspects of an object's appearance, such as the presence of component parts. We exploit locality-sensitive hashing to replace the dot-product kernel operator in the convolution with a fixed number of hash-table probes that effectively sample all of the filter responses in time independent of the size of the filter bank... (read more)

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