Outline
• Texture modeling - continued
– Filtering-based approaches
Texture Modeling
• The structures of images
– The structures in images are due to the inter-pixel
relationships
– The key issue is how to characterize the
relationships
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Representing Textures through Filtering
• Filtering
– Convolving an image with a linear filter yields a
representation of the image on a different basis
– The advantage of the transformation is that the
process makes the local structure of the image
“clear”
• A filter can be viewed as a template
• There is a strong response when the local image
pattern looks similar to the filter kernel and a weak
response when it does not
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Representing Textures through Filtering – cont.
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Representing Textures through Filtering – cont.
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Representing Textures through Filtering – cont.
• Gabor filters
– Gabor filters have been widely in texture
modeling
– Mathematically, Gabor filters are optimal in the
sense of local joint spatial/frequency
representation
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Representing Textures through Filtering – cont.
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Representing Textures through Filtering – cont.
• Problems with filtering-based approaches
– The filter response itself does not give rise to a
representation or a model for textures
– Even for homogenous textures, the filter
responses are not homogenous
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Representing Textures through Filtering – cont.
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Representing Textures through Filtering – cont.
• Non-linear smoothing
– In order to derive a more homogenous and
meaningful feature for textures, the filter
responses are then passed through a non-linear
stage
• The hope is that smoothed filter response will be
relative homogenous within a texture region
• This can be used for texture classification, texture
boundary detection, and texture discrimination
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Texture Classification Based on Filtering
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Texture Classification Based on Filtering – cont.
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Texture Classification Based on Filtering – cont.
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Texture Classification Based on Filtering – cont.
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Histograms of Filter Responses as Texture Models
• Histograms as texture features
– For homogenous textures, histograms should not
change very much
• In other words, texture images with similar histograms
of filter responses should look similar
– Heeger and Bergen proposed a texture synthesis
algorithm based on this observation
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Histograms of Filter Responses as Texture Models – cont.
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Histograms of Filter Responses as Texture Models – cont.
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Histograms of Filter Responses as Texture Models – cont.
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Histograms of Filter Responses as Texture Models – cont.
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Histograms of Filter Responses as Texture Models – cont.
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Histograms of Filter Responses as Texture Models – cont.
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Histograms of Filter Responses as Texture Models – cont.
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Non-Parametric Sampling
• Non-parametric sampling
– When we need to decide a pixel value, we
calculate the conditional probability given the
pixel values in the surrounding neighborhood
• This is done by finding the similar surrounding
neighborhood in the given texture
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Non-Parametric Sampling – cont.
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Non-Parametric Sampling – cont.
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Non-Parametric Sampling – cont.
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