Large-Scale Object Recognition Systems

author: Cordelia Schmid, INRIA - The French National Institute for Research in Computer Science and Control
published: Dec. 5, 2008,   recorded: November 2008,   views: 2762
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Description

This paper introduces recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We first analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and leads us to derive a more precise representation based on

  • Hamming embedding (HE) and
  • weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within the inverted file and are efficiently exploited for all images, even in the case of very large datasets.

Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy.

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Reviews and comments:

Comment1 Chen Cheng, May 11, 2009 at 8:45 a.m.:

the slides etvc08_schmid_lsors_01.ppt can't be downloaded.


Comment2 simon, June 4, 2009 at 2:06 a.m.:

speak slower, and sync your lip with the slides

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