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Texture Detection Texture Detection &&
Texture related clustering Texture related clustering
C601 ProjectC601 Project
Jing QinJing Qin
Fall 2003Fall 2003
OutlineOutline
IntroductionIntroduction PCA based texture representationPCA based texture representation Texture detectionTexture detection Texture related image clusteringTexture related image clustering Future worksFuture works
IntroductionIntroduction
What is “textures”?What is “textures”? Webster’s:Webster’s:
• Something composed of closely interwoven Something composed of closely interwoven elementselements
• The structure formed by the threads of a fabricThe structure formed by the threads of a fabric• The visual or tactile surface characteristics and The visual or tactile surface characteristics and
appearance of somethingappearance of something• Etyma: L textura, fr. Textus, (to weave)Etyma: L textura, fr. Textus, (to weave)
Others: grain, pattern of wood, water,graniteOthers: grain, pattern of wood, water,granite
Introduction (Cont.)Introduction (Cont.) CS Definition:CS Definition:
Formalized terms:Formalized terms:• Basic elementsBasic elements
PixelsPixels Small patternsSmall patterns
• Relations (repetition) of elementsRelations (repetition) of elements statisticsstatistics grammargrammar
Descriptive Def:Descriptive Def:• Those similar enough to a set of textures samples Those similar enough to a set of textures samples
would be of the same textureswould be of the same textures• What do we mean by saying: “similar”,then?What do we mean by saying: “similar”,then?
Introduction (Cont)Introduction (Cont)
Statistical Texture Description Statistical Texture Description spatial frequencies spatial frequencies Edge frequenciesEdge frequencies Primitive lengthPrimitive length …………..
Syntactic texture description Syntactic texture description Shape chain grammars Shape chain grammars Graph grammars Graph grammars
Texture RepresentationTexture Representation
PCA (Principal Component Analysis):PCA (Principal Component Analysis): Project the samples (points) perpendicularly Project the samples (points) perpendicularly
onto the axis of ellipsoidonto the axis of ellipsoid Rotates the ellipsoid to be parallel to the Rotates the ellipsoid to be parallel to the
coordinate axescoordinate axes Use the fewer and more important Use the fewer and more important
coordinates to represent the original samplescoordinates to represent the original samples Transforms of PCA:Transforms of PCA:
The first a few eigenvectors of covariance The first a few eigenvectors of covariance matrixmatrix
Texture Representation (Cont.)Texture Representation (Cont.)
How to represent textures using PCA?How to represent textures using PCA? Select primary textures (6,7) we need to Select primary textures (6,7) we need to
consider (manually)consider (manually) Use texture samples (16Use texture samples (16××16 texture images) 16 texture images)
as points in PCAas points in PCA Compute the eigenvector (PCA transform) Compute the eigenvector (PCA transform)
using those 6 or 7 256 dimensional vectors using those 6 or 7 256 dimensional vectors with PCA.with PCA.
Use the Eigen-textures generated through Use the Eigen-textures generated through PCA transform as the texture representationPCA transform as the texture representation
-2.9768 2.5950 0.0013 0.0324 -0.0120 -0.0000
7 primary textures (16*16 blocks) manually selected to compute through PCA
6- dimensional Eigen textures generated for the texture No.1(256-dim converted to 6-dim)
Texture detectionTexture detection
Compare the image to the texture Compare the image to the texture representation (similarity match)representation (similarity match)
Texture detection based on PCATexture detection based on PCA PCA TransformPCA Transform Compare Eigen-images to Eigen-texturesCompare Eigen-images to Eigen-textures
• Euclidean distanceEuclidean distance Texture SegmentationTexture Segmentation
Texture Detection (1st Ver)Texture Detection (1st Ver)Dividing the target image into (overlapping) blocks with the same size as the 7 primary textures, use the PCA transform and compare them to the eigen-textures (compute the euclidean distance)
Only use texture detection, 6 clusters generated
Revised VersionRevised Version
Revised versionRevised version Intuition: reduce the Intuition: reduce the
influence of light conditioninfluence of light condition Calibrate (Generalize) grey Calibrate (Generalize) grey
level with the texture level with the texture sample before using PCAsample before using PCA
• Check the grey level Check the grey level differencedifference
• Reduce/increase the grey Reduce/increase the grey level of the image blocks level of the image blocks accordinglyaccordingly
Better?Better? Problems?Problems?
Texture related image clusteringTexture related image clustering
Color clustering Method usedColor clustering Method used k-meank-mean
Use texture information as fourth Use texture information as fourth dimension (colors as the other three)dimension (colors as the other three)
Add certain weight to the fourth dimension Add certain weight to the fourth dimension (200 or 300, why?)(200 or 300, why?)
Evaluation of textures informationEvaluation of textures information The more two textures are similar to each The more two textures are similar to each
other, the closer their ‘texture’ value should beother, the closer their ‘texture’ value should be
Results of Original K-mean PCA-clustering results (4 dimension, without formalizing grey level)
Final version of clustering
First use (grey level) formalized PCA texture detection, then cluster using k-mean, based on texture information combined with 3 color dimensions,
Future WorksFuture Works Texture is not only repeated elementsTexture is not only repeated elements
Reflectivity & refractivityReflectivity & refractivity Combination of other texture principlesCombination of other texture principles
Samples sizeSamples size LargeLarge SmallSmall
Samples selectionSamples selection Problems with PCA: scalabilityProblems with PCA: scalability
ReferenceReference
Image Processing, Analysis, and Machine Image Processing, Analysis, and Machine VisionVisionMilan Milan SonkaSonka, Vaclav, Vaclav Hlavac Hlavac, and Roger Boyle, and Roger Boyle 1998 1998
http://www.http://www.cscs..berkeleyberkeley..eduedu/~/~dafdaf//bookpagesbookpages/slides.html/slides.html
Merriam-Webster’s Collegiate Merriam-Webster’s Collegiate Dictionary, Dictionary, Tenth EditionTenth Edition