1997
@article{CMT1997,
vgclass = {refpap},
vgproject = {cbir},
author = {N. W. Campbell and W. P. J. Mackeown and B. T Thomas and
T. Troscianko},
title = {Interpreting Image Databases by Region Classification},
journal = {Pattern Recognition},
volume = {30},
number = {4},
pages = {555--563},
month = {April},
year = {1997},
note = {(special edition on image databases)},
url = {http://www.cs.bris.ac.uk/Tools/Reports/Abstracts/1997-campbell.html},
abstract = {This paper addresses automatic interpretation of images of
outdoor scenes for image indexing and retrieval from databases. The
method allows instances of objects from a number of generic classes to
be identified: vegetation, buildings, vehicles, roads, etc., thereby
enabling image databases to be queried on scene content. This is
achieved using a powerful set of image features which are used to train
a neural network classifier. A large database of high-quality colour
images of outdoor scenes developed at Bristol University provides a
ground-truth interpretation of the images, which has enabled a detailed
quantitative analysis of the vision system performance.
The design of the feature set has been inspired by psychophysical
models of vision. Texture is provided in the form of the magnitude
response of a set of isotropic Gabor filters. Colour is represented by
luminance, red/green and yellow/blue information. The shape of a region
is represented by linear combinations of the principal modes of
variation of an approximating polygon. The feature set also includes
contextual information from an initial pixel classification.
Progressive improvements in pixel classification are demonstrated by
the addition of colour and texture information.
The image interpretation technique is very successful and correctly
labels over 90\% of the image area in our database of test images, for
a wide range of object classes. Such a high recognition accuracy
demonstrates that it is possible to interrogate large image databases
by content.},
}