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Image descriptor : a genetic programming approach to multiclass texture classification

conference contribution
posted on 06.12.2017, 00:00 by H Al-Sahaf, M Zhang, M Johnston, Brijesh VermaBrijesh Verma
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 classare 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.

History

Start Page

2460

End Page

2467

Number of Pages

8

Start Date

01/01/2015

Finish Date

01/01/2015

eISSN

1089-778X

ISBN-13

9781479974924

Location

Sendai, Japan

Publisher

IEEE

Place of Publication

Piscataway, NJ.

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

School of Engineering and Technology (2013- ); TBA Research Institute; Victoria University of Wellington;

Era Eligible

Yes

Name of Conference

Congress on Evolutionary Computation