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

Gregory Alan Bolcer greg at bolcer.org
Thu Jul 25 09:19:58 PDT 2013

Useful for everything.


On 7/25/2013 8:50 AM, Eugen Leitl wrote:
> http://research.google.com/pubs/pub40814.html
> Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
> Abstract: 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. To show the effectiveness of the technique, we apply
> it to evaluate 100,000 deformable-part models requiring over a million (part)
> filters on multiple scales of a target image in less than 20 seconds using a
> single multi-core processor with 20GB of RAM. This represents a speed-up of
> approximately 20,000 times - four orders of magnitude - when compared with
> performing the convolutions explicitly on the same hardware. While mean
> average precision over the full set of 100,000 object classes is around 0.16
> due in large part to the challenges in gathering training data and collecting
> ground truth for so many classes, we achieve a mAP of at least 0.20 on a
> third of the classes and 0.30 or better on about 20% of the classes.
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greg at bolcer.org, http://bolcer.org, c: +1.714.928.5476

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