University of Worcester Worcester Research and Publications
 
  USER PANEL:
  ABOUT THE COLLECTION:
  CONTACT DETAILS:

Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors

Al-Sahaf, H., Zhang, M., Al-Sahaf, A. and Johnston, Mark (2017) Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors. IEEE Transactions on Evolutionary Computation, 21 (6). pp. 825-844. ISSN Print: 1089-778X Online: 1941-0026

Full text not available from this repository. (Request a copy)

Abstract

The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.

Item Type: Article
Additional Information:

The full-text cannot be supplied for this item. Please check availability with your local library or Interlibrary Requests Service.

Uncontrolled Keywords: classification, feature extraction, genetic programming, GP, image descriptor, keypoint detector, computation modelling, image edge detection, detectors
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Academic Departments > Institute of Science and the Environment
Related URLs:
Depositing User: Mark Johnston
Date Deposited: 28 Apr 2019 13:46
Last Modified: 28 Apr 2019 13:46
URI: https://eprints.worc.ac.uk/id/eprint/7841

Actions (login required)

View Item View Item
 
     
Worcester Research and Publications is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.