Better matching with fewer features: The selection of useful features
in large database recognition problems
By Panu Turcot
There has been recent progress on the problem of recognizing specific
objects in very large datasets. The most common approach has been based on
the bag-of-words (BOW) method, in which local image features are clustered
into visual words. This can provide significant savings in memory compared
to storing and matching each feature independently. In this paper we take an
additional step to reducing memory requirements by selecting only a small
subset of the training features to use for recognition. This is based on the
observation that many local features are unreliable or represent irrelevant
clutter. We are able to select “useful” features, which are both robust and
distinctive, by an unsupervised preprocessing step that identifies correctly
matching features among the training images. We demonstrate that this
selection approach allows an average of 4% of the original features per
image to provide matching performance that is as accurate as the full set.
In addition, we employ a graph to represent the matching relationships
between images. Doing so enables us to effectively augment the feature set
for each image through merging of useful features of neighboring images. We
demonstrate adjacent and 2-adjacent augmentation, both of which give a
substantial boost in performance.
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