Synthesizing feature agents using evolutionary computation

被引：1

作者：

Bhanu, B ^{[1
]}

Lin, YQ ^{[1
]}

机构：

[1] Univ Calif Riverside, Ctr Res Intelligent Syst, Riverside, CA 92021 USA

来源：

PATTERN RECOGNITION LETTERS | 2004年 / 25卷 / 13期

关键词：

feature synthesis; genetic programming; ROI extraction; smart crossover and smart mutation;

D O I：

10.1016/j.patrec.2004.06.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, genetic programming (GP) with smart crossover and smart mutation is proposed to discover integrated feature agents that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and smart mutation identify and keep the effective components of integrated operators called "agents" and significantly improve the efficiency of GP. Our experimental results show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the regions-of-interest and the learned agents can be applied to extract ROIs in other similar images. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：1519 / 1531

页数：13

共 19 条

[1]

[Anonymous], 1998, Genetic programming: an introduction

[2]

BHANU B, 2002, P GEN EV COMP C JUL, P1003

[3] Detection of urban structures in SAR images by robust fuzzy clustering algorithms: The example of street tracking [J].

Dell'Acqua, F ;

Gamba, P .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2001, 39 (10) :2287-2297

[4]

DHAESELEER P, 1994, P 1994 IEEE WORLD C, V1, P256

[5] Segmentation and classification of vegetated areas using polarimetric SAR image data [J].

Dong, Y ;

Milne, AK ;

Forster, BC .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2001, 39 (02) :321-329

[6]

Gonzalez R.C., 2007, DIGITAL IMAGE PROCES, V3rd

[7] Target detection in SAR imagery by genetic programming [J].

Howard, D ;

Roberts, SC ;

Brankin, R .

ADVANCES IN ENGINEERING SOFTWARE, 1999, 30 (05) :303-311

[8] Depth-dependent crossover for genetic programming [J].

Ito, T ;

Iba, H ;

Sato, S .

1998 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION - PROCEEDINGS, 1998, :775-780

[9] Road detection in spaceborne SAR images using a genetic algorithm [J].

Jeon, BK ;

Jang, JH ;

Hong, KS .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2002, 40 (01) :22-29

[10] A model-based approach to the automatic extraction of linear features from airborne images [J].

Katartzis, A ;

Sahli, H ;

Pizurica, V ;

Cornelis, J .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2001, 39 (09) :2073-2079

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