Small codes and large image databases for recognition

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Small codes and large image databases for recognition

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by Antonio Torralba , Rob Fergus , Yair Weiss

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@MISC{Torralba_smallcodes,
    author = {Antonio Torralba and Rob Fergus and Yair Weiss},
    title = {Small codes and large image databases for recognition},
    year = {}
}

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Abstract

The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition [2], computer graphics [11, 27], personal photo collections, online image search tools. In this paper, our goal is to develop efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices. Our approach uses recently developed machine learning techniques to convert the Gist descriptor (a real valued vector that describes orientation energies at different scales and orientations within an image) to a compact binary code, with a few hundred bits per image. Using our scheme, it

Keyphrases

vassar st little memory object recognition incredibly rich resource different scale handheld device efficient image search gist descriptor standard hardware personal photo collection online image search tool large number compact binary code computer graphic available online orientation energy

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