An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier]
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Bi:2020:CIM,
-
author = "Ying Bi and Bing Xue and Mengjie Zhang",
-
journal = "IEEE Computational Intelligence Magazine",
-
title = "An Effective Feature Learning Approach Using Genetic
Programming With Image Descriptors for Image
Classification [Research Frontier]",
-
year = "2020",
-
volume = "15",
-
number = "2",
-
pages = "65--77",
-
abstract = "Being able to extract effective features from
different images is very important for image
classification, but it is challenging due to high
variations across images. By integrating existing
well-developed feature descriptors into learning
algorithms, it is possible to automatically extract
informative high-level features for image
classification. As a learning algorithm with a flexible
representation and good global search ability, genetic
programming can achieve this. In this paper, a new
genetic programming-based feature learning approach is
developed to automatically select and combine five
existing well-developed descriptors to extract
high-level features for image classification. The new
approach can automatically learn various numbers of
global and/or local features from different types of
images. The results show that the new approach achieves
significantly better classification performance in
almost all the comparisons on eight data sets of
varying difficulty. Further analysis reveals the
effectiveness of the new approach to finding the most
effective feature descriptors or combinations of them
to extract discriminative features for different
classification tasks.",
-
keywords = "genetic algorithms, genetic programming",
-
DOI = "doi:10.1109/MCI.2020.2976186",
-
ISSN = "1556-6048",
-
month = may,
-
notes = "Also known as \cite{9067779}",
- }
Genetic Programming entries for
Ying Bi
Bing Xue
Mengjie Zhang
Citations