Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{Al-Sahaf:2015:EC,

• author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
• title = "Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances",
• journal = "Evolutionary Computation",
• year = "2016",
• volume = "24",
• number = "1",
• pages = "143--182",
• month = "Spring",
• keywords = "genetic algorithms, genetic programming, Local Binary Patterns, One-shot Learning, Image Classification",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/EVCO_a_00146",
• size = "37 pages",
• abstract = "In the Computer Vision and Pattern Recognition fields, image classification represents an important, yet difficult, task to perform. The remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class, is a challenge to build effective computer models to replicate this ability. Recently, we have proposed two Genetic Programming (GP) based methods, One-shot GP and Compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. Ten data sets that vary in difficulty have been used to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that One-shot GP and Compound-GP outperform or achieve comparable results to other competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases",

}

Genetic Programming entries for Harith Al-Sahaf Mengjie Zhang Mark Johnston

Citations