PATSI - Photo Annotation through Similar Images
Michal Stanek, Oskar Maier, Halina Kwasnicka
(Wroclaw University of Technology, Poland)
Abstract:
Automatic Image Annotation (AIA) is an important open problem in computer
vision field, it can improve the performance of images retrieval from
large image collections by text queries. A family of baseline methods
proposed by Makadia et al. (2008) share the assumption that visually
similar images are likely to share the same annotations. The annotation
process relies on transferring labels from a number of the nearest
neighbors. Handicaps are the need to manually set the neighborhood size
and especially the restriction of the annotation length to a fixed,
predetermined size. We present a method for Photo Annotation through
Finding Similar Images (PATSI) based on the hypothesis that similar
images should share a large part of the annotations inspired by Makadia
et al. (2008). We incorporate the nearest neighbor approach and keep our
method as simple as possible. Our method is designed towards solving the
problem of choosing the appropriate length of annotation assigned to the
target image. Additionally we propose a transfer parameter optimization
method which tunes the resulting word count associated with the image,
eliminating the need of manually setting the parameters. On top of this
we investigate a number of interesting and representative transfer
functions and PATSIs sensitivity to a variety of distance measure and
feature set selections. We also present experimental results for
benchmark data sets.