Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Al-Sahaf:2017:ieeeTEC,
-
author = "Harith Al-Sahaf and Mengjie Zhang and
Ausama Al-Sahaf and Mark Johnston",
-
journal = "IEEE Transactions on Evolutionary Computation",
-
title = "Keypoints Detection and Feature Extraction: A Dynamic
Genetic Programming Approach for Evolving
Rotation-invariant Texture Image Descriptors",
-
year = "2017",
-
volume = "21",
-
number = "6",
-
pages = "825--844",
-
month = dec,
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "1089-778X",
-
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7885048",
-
DOI = "doi:10.1109/TEVC.2017.2685639",
-
abstract = "The goodness of the features extracted from the
instances and the number of training instances are two
key components in machine learning, and building an
effective model is largely affected by these two
factors. Acquiring a large number of training instances
is very expensive in some situations such as in the
medical domain. Designing a good feature set, on the
other hand, is very hard and often requires domain
expertise. In computer vision, image descriptors have
emerged to automate feature detection and extraction;
however, domain-expert intervention is typically needed
to develop these descriptors. The aim of this paper is
to use Genetic Programming to automatically construct a
rotation-invariant image descriptor by synthesising a
set of formulae using simple arithmetic operators and
first-order statistics, and determining the length of
the feature vector simultaneously using only two
instances per class. Using seven texture classification
image datasets, the performance of the proposed method
is evaluated and compared against eight domain-expert
hand-crafted image descriptors. Quantitatively, the
proposed method has significantly outperformed, or
achieved comparable performance to, the competitor
methods. Qualitatively, the analysis shows that the
descriptors evolved by the proposed method can be
interpreted.",
-
notes = "Also known as \cite{7885048}",
- }
Genetic Programming entries for
Harith Al-Sahaf
Mengjie Zhang
Ausama Al-Sahaf
Mark Johnston
Citations