Improvement of unsupervised texture classification based on genetic algorithms

Hiroshi Okumura; Yuuki Togami; Kohei Arai

doi:10.1117/12.565776

10 November 2004 Improvement of unsupervised texture classification based on genetic algorithms

Hiroshi Okumura, Yuuki Togami, Kohei Arai

Proceedings Volume 5573, Image and Signal Processing for Remote Sensing X; (2004) https://doi.org/10.1117/12.565776
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain

Abstract

At the previous conference, the authors are proposed a new unsupervised texture classification method based on the genetic algorithms (GA). In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: 1) the determination of the number of classification category; 2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed; 6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. However, this method has not been automated because it requires not only target image but also the number of categories for classification. In this paper, we describe some improvement for implementation of automated texture classification. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.

Citation Download Citation

Hiroshi Okumura, Yuuki Togami, and Kohei Arai "Improvement of unsupervised texture classification based on genetic algorithms", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); https://doi.org/10.1117/12.565776

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image classification

Genetic algorithms

Tantalum

Reliability

Skin

Binary data

Information science

Show All Keywords

Keywords/Phrases

Search In:

Publication Years