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dan
2015-06-15 22:12:02 +02:00
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doc/examples/plot_multiblock_local_binary_pattern.py
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Multi-Block Local Binary Pattern for texture classification
===========================================================
This example shows how to compute multi-block local binary
pattern (MB-LBP) features as well as how to visualize them.
This example shows how to compute multi-block local binary pattern (MB-LBP)
features as well as how to visualize them.
The features are calculated similarly to local binary patterns (LBPs),
except that summed blocks are used instead of individual pixel values.
The features are calculated similarly to local binary patterns (LBPs), except
that summed blocks are used instead of individual pixel values.
MB-LBP is an extension of LBP that can be computed on multiple scales
in constant time using the integral image. 9 equally-sized rectangles
are used to compute a feature. For each rectangle, the sum of the pixel
intensities is computed. Comparisons of these sums to that of the central
rectangle determine the feature, similarly to LBP
(See `LBP <plot_local_binary_pattern.html>`_).
First, we generate an image to illustrate the functioning of MB-LBP:
we take a (9, 9) rectangle and divide it into (3, 3) block,
upon which we then apply MB-LBP.
MB-LBP is an extension of LBP that can be computed on multiple scales in
constant time using the integral image. 9 equally-sized rectangles are used to
compute a feature. For each rectangle, the sum of the pixel intensities is
computed. Comparisons of these sums to that of the central rectangle determine
the feature, similarly to LBP (See `LBP <plot_local_binary_pattern.html>`_).
First, we generate an image to illustrate the functioning of MB-LBP: consider
a (9, 9) rectangle and divide it into (3, 3) block, upon which we then apply
MB-LBP.
"""
from __future__ import print_function