Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances

Created by W.Langdon from gp-bibliography.bib Revision:1.5787

@Article{Al-Sahaf:EC,

• author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mengjie Zhang",
• title = "Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances",
• journal = "Evolutionary Computation",
• note = "Forthcoming",
• keywords = "genetic algorithms, genetic programming, ANN, image descriptor, multi-tree, image classification, feature extraction",
• ISSN = "1063-6560",
• URL = "https://doi.org/10.1162/evco_a_00284",
• DOI = "doi:10.1162/evco_a_00284",
• size = "34 pages",
• abstract = "The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, e.g., corners, line-segments and shapes, in an image and extracting features from those key points. In this paper, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by using a multi-tree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the f",

}

Genetic Programming entries for Harith Al-Sahaf Ausama Al-Sahaf Bing Xue Mengjie Zhang

Citations