1996
@article{LiP1996,
vgclass = {refpap},
vgproject = {cbir},
author = {F. Liu and R.W. Picard},
title = {Periodicity, directionality, and randomness: Wold features
for image modeling and retrieval},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {18},
number = {7},
pages = {722--733},
month = {July},
year = {1996},
abstract = {One of the fundamental challenges in pattern recognition
is choosing a set of features appropriate to a class of problems. In
applications such as database retrieval, it is important that image
features used in pattern comparison provide good measures of image
perceptual similarities. In this paper, we present an image model with
a new set of features that address the challenge of perceptual
similarity. The model is based on the 2D Wold decomposition of
homogeneous random fields. The three resulting mutually orthogonal
subfields have perceptual properties which can be described as
``periodicity'', ``directionality'', and ``randomness'', approximating
what are indicated to be the three most important dimensions of human
texture perception. The method presented here improves upon earlier
Wold-based models in its tolerance to a variety of local
inhomogeneities which arise in natural textures and its invariance
under image transformation such as rotation.
An image retrieval algorithm based on the new texture model is
presented. Different types of image features are aggregated for
similarity comparison by using a Bayesian probabilistic approach. The
effectiveness of the Wold model at retrieving perceptually similar
natural textures is demonstrated in comparison to that of two other
well-known pattern recognition methods. The Wold model appears to
offer a perceptually more satisfying measure of pattern similarity
while exceeding the performance of these other methods by traditional
pattern recognition criteria. Examples of natural scene Wold texture
modeling are also presented.},
}