speaker: yi-chun ke adviser: bo-chi lai ku-yaw chang
TRANSCRIPT
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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/
IntoToNeuralNets.html2009/10/06