Transcript PPT - The Prague Texture Segmentation Datagenerator and

Texture Segmentation Based on Voting of Blocks,
Bayesian Flooding and Region Merging
C. Panagiotakis(1), I. Grinias(2) and G. Tziritas(3)
Presenter: Dr. Costas Panagiotakis, Assistant Professor,
(1) Business Administrator Administration Dep., TEI of Crete, Agios Nikolaos, Greece
(2): Geoinformatics and Surveying Dep., TEI of Serres, Serres, Greece
(3): Computer Science Department, University Of Crete, Greece
22th International Conference on Pattern Recognition 07-07-2009
Introduction: Related Work
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Segmentation of images is quite important for many applications, such as content based
image retrieval and object recognition.
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In our previous work [1], we proposed a framework that performs automatic segmentation
of images, knowing only the number of regions, which involves feature extraction and
classification in feature space, followed by flooding (PMCFA) and merging in spatial
domain.
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PMCFA has been also successfully applied on interactive image segmentation [2], where
the goal is to classify the image pixels into foreground and background classes, when some
foreground and background markers are given.
[1] C. Panagiotakis, I. Grinias and G. Tziritas, Natural Image Segmentation based on Tree Equipartition, Bayesian
Flooding and Region Merging, IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 2276 - 2287, Aug. 2011.
[2] C. Panagiotakis, H. Papadakis, E. Grinias, N. Komodakis, P. Fragopoulou and G. Tziritas, Interactive Image
Segmentation Based on Synthetic Graph Coordinates, Pattern Recognition, vol. 46, no. 11, pp. 2940-2952, Nov. 2013.
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Introduction: Contribution
1. The proposed method uses features that are optimized and
tested for textured images.
2. We solve the problem to find subset of blocks that represent well
the whole dataset of blocks by a new framework that takes into
account the blocks’ similarity and topology. The representative
blocks are used to extract the features for each class.
3. The proposed method automatically computes the number of
classes regions by a new criterion that takes into account the
average likelihood per pixel of the classification map and
penalizes the complexity of the regions boundaries. In [1-2] the
number of classes were given.
[1] C. Panagiotakis, I. Grinias and G. Tziritas, Natural Image Segmentation based on Tree Equipartition, Bayesian
Flooding and Region Merging, IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 2276 - 2287, Aug. 2011.
[2] C. Panagiotakis, H. Papadakis, E. Grinias, N. Komodakis, P. Fragopoulou and G. Tziritas, Interactive Image
Segmentation Based on Synthetic Graph Coordinates, Pattern Recognition, vol. 46, no. 11, pp. 2940-2952, Nov. 2013.
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System Overview
• The proposed framework can operate completely unsupervised.
• In this work, MINR = 3 and MAXR = 15
Main Steps:
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Methodology: Feature Selection
• The image is divided into overlapping blocks (50% overlapping).
• 64 × 64 block for a frame of 512 × 512 pixels is used.
• We use the three components of Lab color space to represent the color
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• The last component is the energy of horizontal and vertical components from
wavelet transform using the fourth-order binomial filter [1 4 6 4 1]/16. We
show that these components of wavelet transform suffice to represent well the
texture information.
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Methodology: MAXR BLOCKS SELECTION
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Goal: Select the MAXR most representative
image blocks taking into account the blocks
similarity and topology.
Main Steps:
• The M image blocks are represented by a
graph G, whose weights are given by the
Mallows distance of three color components
and of the texture component of the
corresponding blocks (4-connections
neighborhood).
• Next, we find the MxM matrix of all shortest
paths in graph G – taking into account
similarity and topology.
• Similar results are also obtained and by
using the MST of G instead of G.
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MxM matrix of all shortest paths in graph G
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Methodology: MAXR BLOCKS SELECTION
The proposed MAXR BLOCKS
SELECTION is inspired from [1].
MAXR Selected Blocks
Main Steps:
• The first block is given by the block of
minimum mean distance from others
(centroid).
• Next, we repeat MAXR-1 times the
following procedure:
o The next block is given taking into
account the current selected blocks.
o We get the block that has low
distances from others (non selected
blocks) and high distance from the
selected blocks.
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[1] C. Panagiotakis, Clustering via Voting Maximization, Journal of Classification, 2014 (accepted).
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Flooding Process for Class Propagation PMCFA (1/3)
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Definition of a topographic map for each class k using the computed conditional
probabilities:
– Height of pixel s represents the dissimilarity of s from class k, defined as
–ln P{k|ξ(s)}
where P{k|ξ(s)} is the a-posteriori probability of class k given the feature vector ξ(s).
Class
Class
[1] C. Panagiotakis, I. Grinias and G. Tziritas, Natural Image Segmentation based on Tree Equipartition, Bayesian
Flooding and Region Merging, IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 2276 - 2287, Aug. 2011.
8
Flooding Process for Class Propagation PMCFA (2/3)

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Path cost Ci(s0,s) between pixels s and s0: the maximum height of
pixels in that path.
Topographic distance δk(s) between s and s0: the minimum cost of
paths between s and s0.
[1] C. Panagiotakis, I. Grinias and G. Tziritas, Natural Image Segmentation based on Tree Equipartition, Bayesian
Flooding and Region Merging, IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 2276 - 2287, Aug. 2011.
Flooding Process for Class Propagation PMCFA (3/3)
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Priority Multi-Class Flooding Algorithm:
Input
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Topographic map and
initial regions of high
confidence per class.
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Competitive growing for both the
computation of topographic map
and pixel labeling.
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Flooding stops when all image
pixels are labeled.
Class
Original image
Topographic map
PMCFA Result
[1] C. Panagiotakis, I. Grinias and G. Tziritas, Natural Image Segmentation based on Tree Equipartition, Bayesian
Flooding and Region Merging, IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 2276 - 2287, Aug. 2011.
4
Merging Process
• Usually, the number of computed regions is greater
than the real number of classes.
• A merging state solves this over-segmentation
problem.
• We have used a greedy algorithm that iteratively
merges the regions taking into account the
dissimilarity in appearance of the segments and the
gradient on region boundaries.
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Selection of the appropriate segmentation Map
We select the segmentation that minimizes a
criterion C(k) = FS(K) + λ PC(k) taking into
account
– the average likelihood per pixel of the classification
map (FS(K)) and
– penalizes the complexity of the regions boundaries
(PC(K)) that is computed from the points with
curvature higher than 0.5 multiplied by a
normalization factor.
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Experimental Results on Prague Texture Segmentation Benchmark
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Conclusions
An unsupervised segmentation algorithm is proposed which
combines
• color and texture features,
• region features and
• topology.
yielding high performance results.
Results on Prague Texture Segmentation Benchmark:
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