Optimizing Texture Feature Extraction in Image Analysis by Using Experimental Design Theory

S. A. Orjuela Vargas1, R. de Keyser2, and W. Philips1

11th FIRW PhD symposium | December 2010

1.Department of Telecommunications and Information Processing (TELIN-IPI-IBBT), Gent UniversitySint-Pietersnieuwstraat 41, B-9000 Gent, Belgium.

2. Department of Electrical Energy, Systems and Automation (EeSA) , Gent University, Belgium

http://telin.ugent.be/~seraleov seraleov@telin.ugent.be

A planning phase using experimental design theory add reliability to image analysis results

[2] S. A. Orjuela, E. Vansteenkiste, F. Rooms, S. De Meulemeester, R. De Keyser, and W. Philips. Evaluationof the wear label description in carpets by using local binary pattern techniques. Textile Research Journal, 2010.

To extract texture features several techniques are evaluated

High amount of data must be analyzed

Optimal set of features are commonly chosen by “best guess approach”

Techniques dependences are not evaluated

Characteristics from image outcomes are quantified in response variables to compare techniques

No guarantee of optimalityNo reliability of results and inferences

Technique dependences are similarly detected

Coocurrence Matrices

Laws’ Energy

Local Binary Patterns

Wavelets

Significant differences are identified when probability results are smaller than a given

Technique A

Technique B

Technique C

IMAGEQuantified Characteristics

Response variables

Linear Model

Analysis of VarianceANOVA

Comparison is performed by using ANalysis Of VAriance (ANOVA)

The relationship between wear labels and features must be at least linear-ranked

If so, the distinction between consecutive wear labels must be maximized

Application: Automatic wear label assessment for carpets, comparison of three techniques

Two response variables are quantified

1. Monotonicity (): Based on the Spearman’s rank correlation

2. Number of consecutive wear labels that can be statistically distinguished ():Based on the Tukey test

A test for multiple comparison must be further performed

LBPRMC technique performs better than the others for both response variables

Significance differences detected for both response variables at 99% of confidence

Wear

Lab

els

[1] S. A. Orjuela, Experimetal Design Theory as a Tool for Optimizing Feature Extraction in Image Analysis, Tutorial, STSIVA 2010.

Features

Texture is a pattern describing variations in a surface at scales smaller than the scale of interest

Texture analysis has four major areas of interest

medical imaging Industrial inspection

Remote sensing Image retrieval

Optimal combinations of techniques are found

Carpets are certified according to their capability in retaining the original appearance

Distinction of different degrees of wear by texture analysis in photographs

Worn samples are compared to original samples assessing the change in appearance with wear labels

Texture Analysis

Experimental Design Theory in Image Analysis

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