1999
@article{HaV1999,
vgclass = {refpap},
vgproject = {cbir},
author = {Niels Haering and da Vitoria Lobo, Niels},
title = {Features and Classification Methods to Locate Deciduous
Trees in Images},
journal = {Computer Vision and Image Understanding (special issue on content-based access for image
and video libraries)},
volume = {75},
number = {1/2},
pages = {133--149},
month = {July/August},
year = {1999},
url = {http://www.cs.ucf.edu/\~{}haering/publications/cviu99.ps.gz},
abstract = {We compare features and classification methods to locate
deciduous trees in images. From this comparison we conclude that a
back-propagation neural network achieves better classification results
than the other classifiers we tested. Our analysis of the relevance of
51 features from seven feature extraction methods based on the
graylevel co-occurrence matrix, Gabor filters, fractal dimension,
steerable filters, the Fourier transform, entropy, and color shows that
each feature contributes important information. We show how we obtain a
13-feature subset that significantly reduces the feature extraction
time while retaining most of the complete feature set's power and
robustness. The best subsets of features were found to be combinations
of features of each of the extraction methods. Methods for
classification and feature relevance determination that are based on
the covariance or correlation matrix of the features (such as
eigenanalyses or linear or quadratic classifiers) generally cannot be
used, since even small sets of features are usually highly linearly
redundant, rendering their covariance or correlation matrices too
singular to be invertible. We argue that representing deciduous trees
and many other objects by rich image descriptions can significantly aid
their classification. We make no assumptions about the shape, location,
viewpoint, viewing distance, lighting conditions, and camera
parameters, and we only expect scanning methods and compression schemes
to retain a ``reasonable'' image quality.},
}