Abstract
This paper addresses the problem of recognizing objects in large
image databases. The method is based on local characteristics which are
invariant to similarity transformations in the image. These
characteristics are computed at automatically detected keypoints using
the greyvalue signal. The method therefore works on images such as
paintings for which geometry based recognition fails. Due to the
locality of the method, images can be recognized being given part of an
image and in the presence of occlusions. Applying a voting algorithm and
semi-local constraints makes the method robust to noise, scene clutter
and small perspective deformations. Experiments show an efficient
recognition for different types of images. The approach has been
validated on an image database containing 1020 images, some of them
being very similar by structure, texture or shape
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