Binary image classification using genetic programming based on local binary patterns
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Al-Sahaf:2013:IVCNZ,
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author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
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title = "Binary image classification using genetic programming
based on local binary patterns",
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booktitle = "28th International Conference of Image and Vision
Computing New Zealand (IVCNZ 2013)",
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year = "2013",
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pages = "220--225",
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address = "Wellington",
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month = nov,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, computer
vision, image classification, learning (artificial
intelligence), statistical analysis, ANOVA, GP based
methods, LBP, SVM, binary image classification,
computer vision, image descriptor, learning instances,
local binary patterns, machine learning, nonGP methods,
one-way analysis of variance, support vector machine,
wrapped classifiers, Accuracy, Analysis of variance,
Feature extraction, Histograms, Support vector
machines, Training, Vectors",
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DOI = "doi:10.1109/IVCNZ.2013.6727019",
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size = "6 pages",
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abstract = "Image classification represents an important task in
machine learning and computer vision. To capture
features covering a diversity of different objects, it
has been observed that a sufficient number of learning
instances are required to efficiently estimate the
models' parameter values. In this paper, we propose a
genetic programming (GP) based method for the problem
of binary image classification that uses a single
instance per class to evolve a classifier. The method
uses local binary patterns (LBP) as an image
descriptor, support vector machine (SVM) as a
classifier, and a one-way analysis of variance (ANOVA)
as an analyser. Furthermore, a multi-objective fitness
function is designed to detect distinct and informative
regions of the images, and measure the goodness of the
wrapped classifiers. The performance of the proposed
method has been evaluated on six data sets and compared
to the performances of both GP based (Two-tier GP and
conventional GP) and non-GP (Naive Bayes, Support
Vector Machines and hybrid Naive Bayes/Decision Trees)
methods. The results show that a comparable or
significantly better performance has been achieved by
the proposed method over all methods on all of the data
sets considered.",
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notes = "also known as \cite{6727019}",
- }
Genetic Programming entries for
Harith Al-Sahaf
Mengjie Zhang
Mark Johnston
Citations