A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Atkins:2011:ADIGPAtAFEfIC,

• title = "A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification",
• author = "Daniel Atkins and Kourosh Neshatian and Mengjie Zhang",
• pages = "238--245",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, automatic image feature extraction, baseline system, classifier system, domain independent genetic programming, human-extracted features, image classification, feature extraction, image classification",
• DOI = "doi:10.1109/CEC.2011.5949624",
• abstract = "In this paper we explore the application of Genetic Programming (GP) to the problem of domain-independent image feature extraction and classification. We propose a new GP-based image classification system that extracts image features autonomously, and compare its performance against a baseline GP-based classifier system that uses human-extracted features. We found that the proposed system has a similar performance to the baseline system, and that GP is capable of evolving a single program that can both extract useful features and use those features to classify an image.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Daniel L Atkins Kourosh Neshatian Mengjie Zhang

Citations