Description: Gabor texture descriptor have gained much attention for
different aspects of computer vision and pattern recognition.
Recently, on the rayleigh nature of Gabor filter outputs
Rayleigh model Gabor texture descriptor is proposed.
In this paper, we investigate the performance of these two
Gabor texture descriptor in texture classification. We built
a texture classification system based on BPNN, and use the
corresponding feature vector from traditional Gabor texture
descriptor or Rayleigh model one as input of BPNN. We use
three datasets from the Brodatz album database. For all
the three datasets, the original texture images are subdivided
into non-overlapping samples of size 32 × 32. 50
of the total samples are used for training and the rest are
used for testing. We compare the system training time and
recognition accuracy between two Gabor texture descriptor.
The experimental results show that, it takes more time when
using Rayleigh model Gabor texture descriptor than tr
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LBSL2.C
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LBSL10.C
LBSL10.DAT
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LCHII.C
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LCOSS.C
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LELP1.C
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LGAM1.C
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LSTDT.C
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LBETA.C
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LBSL1.C
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