Genetic programming for evolving figure-ground segmentors from multiple features
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Liang:2017:ASC,
-
author = "Yuyu Liang and Mengjie Zhang and Will N. Browne",
-
title = "Genetic programming for evolving figure-ground
segmentors from multiple features",
-
journal = "Applied Soft Computing",
-
volume = "51",
-
pages = "83--95",
-
year = "2017",
-
ISSN = "1568-4946",
-
DOI = "doi:10.1016/j.asoc.2016.07.055",
-
URL = "http://www.sciencedirect.com/science/article/pii/S156849461630391X",
-
abstract = "Figure-ground segmentation is a crucial preprocessing
step for many image processing and computer vision
tasks. Since different object classes need specific
segmentation rules, the top-down approach, which learns
from the object information, is more suitable to solve
segmentation problems than the bottom-up approach. A
problem faced by most existing top-down methods is that
they require much human work/intervention, meanwhile
introducing human bias. As genetic programming (GP)
does not require users to specify the structure of
solutions, we apply it to evolve segmentors that can
conduct the figure-ground segmentation automatically
and accurately. This paper aims to determine what kind
of image information is necessary for GP to evolve
capable segmentors (especially for images with high
variations, e.g. varied object shapes or cluttered
backgrounds). Therefore, seven different terminal sets
are exploited to evolve segmentors, and images from
four datasets (bitmap, Brodatz texture, Weizmann and
Pascal databases), which are increasingly difficult for
segmentation tasks, are selected for testing. Results
show that the proposed GP based method can be
successfully applied to diverse types of images. In
addition, intensity based features are not sufficient
for complex images, whereas features containing
spectral and statistical information are necessary.
Compared with four widely-used segmentation techniques,
our method obtains consistently better segmentation
performance.",
-
keywords = "genetic algorithms, genetic programming, Figure-ground
segmentation, Intensity based features, Gabor
features",
- }
Genetic Programming entries for
Yuyu Liang
Mengjie Zhang
Will N Browne
Citations