Author : David A. Clausi, Yongping Zhao Source : Computers & Geosciences 29 (2003) 837-850

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Grey level co-occurrence integrated algoritm (GLCIA) :

a superior computational method to rapid determine

co-occurrence probability texture features

Author : David A. Clausi, Yongping Zhao

Source : Computers & Geosciences 29 (2003) 837-850

Speaker : Kai-Hung Chen Date : Dec. 8, 2004

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Outline

Introduction

Method

Experiments

Conclusions

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Introduction

GLCM

GLCIA GLCHS GLCHH

GLCHSH GLCHDH

Statics for co-occurrence probability

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G=4 (0-3) θ=0 and 180 D=120 possible co-occurrence pairs

GLCM(Gray level co-occurrence matrix)

Ex:(3.2) appears 2 times in the matrix

Probability:0.1

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Statics for co-occurrence probability

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Method GLCHS

(Gray level co-occurrence hybrid structure)

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Method GLCHH(Gray level co-occurrence hybrid histogram)

A:normalized sum histogram

B:normailized difference histogram

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Method GLCHSH(Gray level co-occurrence hybrid sum histogram)

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Method GLCHDH(Gray level co-occurrence hybrid difference histogram)

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Experiment 1/5

Computational time:μs/sample8 statics:DIS,CON,IDM,INV,COR,UNI,ENT,MAX5 statics: CON,IDM,INV,DIS,COR4 statics: CON,IDM,INV,DIS

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Experiment 2/5

8 statics 5 statics 4 statics

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Experiment 3/5

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Experiment 4/5

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Experiment 5/5

image size:1000*1000θ=0,90,180 and 270Processor:2.0 GHz

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Conclusions

Quickly calculate co-occurrence probability

Especially for large-scale remote-sensing image