Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Al-Sahaf:2015:GECCO,
-
author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
-
title = "Evolutionary Image Descriptor: A Dynamic Genetic
Programming Representation for Feature Extraction",
-
booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
-
year = "2015",
-
editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
-
isbn13 = "978-1-4503-3472-3",
-
pages = "975--982",
-
keywords = "genetic algorithms, genetic programming",
-
month = "11-15 " # jul,
-
organisation = "SIGEVO",
-
address = "Madrid, Spain",
-
URL = "http://doi.acm.org/10.1145/2739480.2754661",
-
DOI = "doi:10.1145/2739480.2754661",
-
publisher = "ACM",
-
publisher_address = "New York, NY, USA",
-
abstract = "Texture classification aims at categorising instances
that have a similar repetitive pattern. In computer
vision, texture classification represents a fundamental
element in a wide variety of applications, which can be
performed by detecting texture primitives of the
different classes. Using image descriptors to detect
prominent features has been widely adopted in computer
vision. Building an effective descriptor becomes more
challenging when there are only a few labelled
instances. This paper proposes a new Genetic
Programming (GP) representation for evolving an image
descriptor that operates directly on the raw pixel
values and uses only two instances per class. The new
method synthesises a set of mathematical formulas that
are used to generate the feature vector, and the
classification is then performed using a simple
instance-based classifier. Determining the length of
the feature vector is automatically handled by the new
method. Two GP and nine well-known non-GP methods are
compared on two texture image data sets for texture
classification in order to test the effectiveness of
the proposed method. The proposed method is also
compared to three hand-crafted descriptors namely
domain-independent features, local binary patterns, and
Haralick texture features. The results show that the
proposed method has superior performance over the
competitive methods.",
-
notes = "Also known as \cite{2754661} GECCO-2015 A joint
meeting of the twenty fourth international conference
on genetic algorithms (ICGA-2015) and the twentith
annual genetic programming conference (GP-2015)",
- }
Genetic Programming entries for
Harith Al-Sahaf
Mengjie Zhang
Mark Johnston
Citations