Comparison of two gabor texture descriptor for texture classification紋理分類法中使用兩種賈柏紋理描述子進行比對

Speaker: Yi-Chun Ke

Adviser: Bo-Chi Lai

Ku-Yaw Chang

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Outline

Introduction Material and Method Results Conclusion

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Introduction

Traditional Garbo texture description two-dimensional Gabor function m(x, y) = |gmn(x, y) i(x, y)|∗

μ ： mean δ： standard deviation s： scale k： orientation Descriptors=2 × s × k + 2

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Introduction

Rayleigh Garbo texture description 1-D Gabor function m(x) = |gmn(x) i(x)|∗

s： scale k： orientation Descriptors=s × k + 2

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Introduction

Back propagation neural network(BPNN)

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Method

Traditional Gabor texture descriptor S=4 scales K=6 orientations Descriptors=2 × s × k + 2=50

Rayleigh model Gabor texture descriptor S=4 scales K=6 orientations Descriptors= s × k + 2=26

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Method

Back propagation neural network(BPNN) input nodes number:50 or 26 output nodes number: 4 hidden nodes: 10

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Material

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Data 2

Data 1

Data 3

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Results

dataset 1

traditional Gabor descriptordataset 1

Rayleigh model Gabor descriptor

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Resultsdataset 2

traditional Gabor descriptor

dataset 2

Rayleigh model Gabor descriptor

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Resultsdataset 3

traditional Gabor descriptordataset 3

Rayleigh model Gabor descriptor

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Results

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Conclusion

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Rayleigh Traditional

Training time More Less

Computational expense Less More

Accuracy Low High

Lose some performance compared Easy Hard

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References James A. Freeman and David M. Skapura, Netuoral Networks

Algorithms,Applications,and Programming Techniques,1991,90-93

Sitaram Bhagavathy, Jelena Te si c, and B. S. Manjunath, On the Rayleigh Nature of Gabor Filter Outputs, Digital Object Identifier 10.1109/ICIP Volume 3,2005, I11 – 745- I11 – 748

Xu Zhan, Xingbo Sun, Lei Yuerong, Comparison of two gabor texture descriptor for texture classification , Information Engineering, 2009. ICIE '09. WASE International Conference on Volume 1, 2009, 52 – 56

Technology Exponent http://www.tek271.com/?about=docs/neuralNet/

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