Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation:
Serdica Journal of Computing, Vol. 8, No 1, (2014), 15p-28p
Abstract:
Image classification is an essential problem for content based
image retrieval and image processing. Visual properties can be extracted
from images in the form of MPEG-7 descriptors. Statistical methods can
use these properties as features and be used to derive an effective method
of classifying images by evaluating a minimal number of properties used in
the MPEG-7 descriptor. Classification by artist, artistic movement, and indoor/outdoor
setting is examined using J48, J48 graft, best first, functional,
and least absolute deviation tree algorithms. An improved accuracy of 11%
in classification of artist and 17% in classification of artistic movement over
previous work is achieved using functional trees. In addition classification
by indoor/outdoor setting shows that the method can be applied to new
categories. We present an analysis of generated decision trees that shows
edge histogram information is most prominent in classification of artists and
artistic movements, while scalable color information is most useful for
classification of indoor/outdoor setting.
Description:
ACM Computing Classification System (1998): I.4.9, I.4.10.