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A One-Shot Learning Approach to Image Classification Using Genetic Programming

  • Harith Al-Sahaf
  • Mengjie Zhang
  • Mark Johnston
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)

Abstract

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features.

Keywords

Genetic Programming Local Binary Patterns Image Classification One-shot Learning 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Harith Al-Sahaf
    • 1
  • Mengjie Zhang
    • 1
  • Mark Johnston
    • 1
  1. 1.Evolutionary Computation Research GroupVictoria University of WellingtonWellingtonNew Zealand

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