research-article

Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction

Authors:

Harith Al-Sahaf,

Mark JohnstonAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 975 - 982

https://doi.org/10.1145/2739480.2754661

Published: 11 July 2015 Publication History

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.

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Cited By

Sabar NTurky ASong ATakadama K(2018)A genetic programming based iterated local search for software project schedulingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205557(1364-1370)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205557
Al-Sahaf HZhang MAl-Sahaf AJohnston M(2017)Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image DescriptorsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2017.268563921:6(825-844)Online publication date: Dec-2017
https://doi.org/10.1109/TEVC.2017.2685639

Index Terms

Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
        Matching
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming

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Published In

cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 11 July 2015

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GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Sabar NTurky ASong ATakadama K(2018)A genetic programming based iterated local search for software project schedulingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205557(1364-1370)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205557
Al-Sahaf HZhang MAl-Sahaf AJohnston M(2017)Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image DescriptorsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2017.268563921:6(825-844)Online publication date: Dec-2017
https://doi.org/10.1109/TEVC.2017.2685639

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