Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Al-Sahaf:2015:CEC,

• author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston and Brijesh Verma",
• title = "Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification",
• booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)",
• year = "2015",
• editor = "Yadahiko Murata",
• pages = "2460--2467",
• address = "Sendai, Japan",
• month = "25-28 " # may,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4799-7491-7",
• DOI = "doi:10.1109/CEC.2015.7257190",
• size = "8 pages",
• abstract = "Texture classification is an essential task in computer vision that aims at grouping instances that have a similar repetitive pattern into one group. Detecting texture primitives can be used to discriminate between materials of different types. The process of detecting prominent features from the texture instances represents a cornerstone step in texture classification. Moreover, building a good model using a few training instances is difficult. In this study, a genetic programming (GP) descriptor is proposed for the task of multiclass texture classification. The proposed method synthesises a set of mathematical formulas relying on the raw pixel values and a sliding window of a predetermined size. Furthermore, only two instances per class are used to automatically evolve a descriptor that has the potential to effectively discriminate between instances of different textures using a simple instance-based classifier to perform the classification task. The performance of the proposed approach is examined using two widely-used data sets, and compared with two GP-based and nine well-known non-GP methods. Furthermore, three hand-crafted domain-expert designed feature extraction methods have been used with the non-GP methods to examine the effectiveness of the proposed method. The results show that the proposed method has significantly outperformed all these other methods on both data sets, and the new method evolves a descriptor that is capable of achieving significantly better performance compared to hand-crafted features.",
• notes = "1340 hrs 15390 CEC2015",

}

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

Citations