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