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A multitree genetic programming representation for automatically evolving texture image descriptors

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conference contribution
posted on 28.10.2020, 04:43 by Harith Al-Sahaf, Bing Xue, Mengjie Zhang
© Springer International Publishing AG 2017. Image descriptors are very important components in computer vision and pattern recognition that play critical roles in a wide range of applications. The main task of an image descriptor is to automatically detect micro-patterns in an image and generate a feature vector. A domain expert is often needed to undertake the process of developing an image descriptor. However, such an expert, in many cases, is difficult to find or expensive to employ. In this paper, a multitree genetic programming representation is adopted to automatically evolve image descriptors. Unlike existing hand-crafted image descriptors, the proposed method does not rely on predetermined features, instead, it automatically identifies a set of features using a few instances of each class. The performance of the proposed method is assessed using seven benchmark texture classification datasets and compared to seven state-of-the-art methods. The results show that the new method has significantly outperformed its counterpart methods in most cases.

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Preferred citation

Al-Sahaf, H., Xue, B. & Zhang, M. (2017, January). A multitree genetic programming representation for automatically evolving texture image descriptors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (10593 LNCS pp. 499-511). Springer International Publishing. https://doi.org/10.1007/978-3-319-68759-9_41

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10593 LNCS

Publication or Presentation Year

01/01/2017

Pagination

499-511

Publisher

Springer International Publishing

Publication status

Published

ISSN

0302-9743

eISSN

1611-3349

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