Review Dec, 2001
Workpackage 4Workpackage 4Image Analysis AlgorithmsImage Analysis AlgorithmsProgress Update Dec. 2001Progress Update Dec. 2001
Kirk Martinez, Paul Lewis, David Duplaw, Fazly Abbas, Faizal Fauzi, Mike Westmacott, Marc Chiaverini
Intelligence, Agents and Multimedia Research GroupDepartment of Electronics and Computer ScienceUniversity of SouthamptonUK
Review Dec, 2001
OverviewOverview
•Grey level Histogram
•Texture matching and texture segmentation
•Query by Low Quality Images
•MNS
•colour clustering
•craquelure detection
•Query by Sketch
Review Dec, 2001
Progress on TextureProgress on TextureSegmentation and ClassificationSegmentation and Classification
• Texture in image processing is concerned with repeating patterns
• Work on texture is currently concentrating on wavelets
• Wavelet transforms analyse the image according to scale and frequency
• Transforms can use different decomposition strategies and different base wavelet functions (cf Fourier which uses sines and cosines only)
Review Dec, 2001
Segmentation for Texture IndexingSegmentation for Texture Indexing
• Idea is to divide the image into major regions of homogeneous texture
• Then store representation of each significant texture so that images containing similar textures can be retrieved
• eg we have an image of a textile. We may wish to ask, “are there other images containing a similar textile pattern?”
• Texture may also be a useful contributing key for style classification
Review Dec, 2001
Query by Low Quality ImagesQuery by Low Quality Imageseg Faxeseg Faxes
• Modified the standard wavelet retrieval to use all but the lowest frequency coefficient
• Using a set of 19 faxes we evaluated retrieval by fax using a database of 150 images including the originals for the 19 fax images.
Review Dec, 2001
Using Daubechies WaveletsUsing Daubechies Wavelets
Ranking PWT Modified PWT
Top 5 3 9
6-10 5 3
11-20 4 2
21-30 1 3
31-40 1 0
Other 5 2
Review Dec, 2001
Fax Queries and Database Fax Queries and Database ImageImage
Review Dec, 2001
For all decomposition levels
Image
Pyramid-StructuredWavelet Transform (PWT)Algorithm
Specify decompositionlevels (pyramid depth)
Filter the LL bandhorizontally and
vertically to produceanother 4 subbands
Moredecomposition
levels?
Compute energy inlow-high (LH), high-low
(LH) and high-high(HH) bands
Add energiesto feature
vector
Compute the energy inthe low-low (LL) band
Add energy tofeature vector
Store/match featurevector
Modified PWT forfax?
Yes
No
End
Pyramiddecomposition
Sample fax
Sample texture
Filter the whole imagehorizontally and
vertically to produce 4subbands
Yes
No
DATABASE
V1V2V3V4
.
.
.
.VN
Feature Vector
Review Dec, 2001
Texture Segmentationusing PWT
Image
For all decomposition levels
Next level processing(feature extraction,
retrieval etc)End
Apply the PWTalgorithm to the image
Use all 4 subbands from thesmallest scale to build a 4dimensional vector space
Segment the vector spaceusing clustering techniques,yielding a label image of size
nxn
Expand the label image to bethe same size with next level's
subbands.Expansion formula: n=2n
Image has thesame size with the
original?
Use the expanded label imagetogether with the 3 subbands ofthe next scale to build another 4
dimensional vector space
No
Post-processing of thesegmented image
Yes
First LevelSegmentation
Second Level
Final Level
Third Level
After post-processing
Segmentation oftextured image
using 3 leveldecomposition
Review Dec, 2001
MNS- Multi-Nodal SignatureMNS- Multi-Nodal Signature
• Uses colour pair patches as key for matching
• Original version only used presence of a colour pairs and no real scope for indexing
• Now exploring use of quantised colour pairs, an indexing strategy and use of frequency of occurrence within an image and inverse of document frequency as weightings.
Review Dec, 2001
Query By SketchQuery By Sketch
Review Dec, 2001
Colour Space CusteringColour Space Custering
Review Dec, 2001
Identifying a clusterIdentifying a cluster
Review Dec, 2001
Labelling an image with pigmentLabelling an image with pigment
Review Dec, 2001
Crack DetectionCrack Detection
Original image
Vertical + horizontal detection
diagonal detection Detected cracks
Review Dec, 2001
cracks: another examplecracks: another example
• Next stage is to classify them
