In this paper, we review most major filtering approaches to texture feature extraction and perform a comparative study. Filtering approaches included are Laws masks (1980), ring/wedge filters, dyadic Gabor filter banks, wavelet transforms, wavelet packets and wavelet frames, quadrature mirror filters, discrete cosine transform, eigenfilters, optimized Gabor filters, linear predictors, and optimized finite impulse response filters. The features are computed as the local energy of the filter responses. The effect of the filtering is highlighted, keeping the local energy function and the classification algorithm identical for most approaches. For reference, comparisons with two classical nonfiltering approaches, co-occurrence (statistical) and autoregressive (model based) features, are given. We present a ranking of the tested approaches based on extensive experiments.

Filtering for texture classification: a comparative study

Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.

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General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

Image processing and computer vision are currently hot topics with undergraduates and professionals alike. "Feature Extraction and Image Processing" provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques and fundamentals in a clear and concise manner. Readers can develop working techniques, with usable code provided throughout and working Matlab and Mathcad files on the web. Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals.The new edition includes: a new coverage of curvature in low-level feature extraction (SIFT and saliency) and features (phase congruency); geometric active contours; morphology; and camera models and an updated coverage of image smoothing (anistropic diffusion); skeletonization; edge detection; curvature; and shape descriptions (moments). It is an essential reading for engineers and students working in this cutting edge field. It is an ideal module text and background reference for courses in image processing and computer vision. It features a companion website that includes worksheets, links to free software, Matlab files, solutions and new demonstrations.

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Feature Extraction and Image Processing

Image analysis by bidimensional empirical mode decomposition

Methods for digital-image texture analysis are reviewed based on available literature and research work either carried out or supervised by the authors The review has been prepared on request of Dr Richard Lerski, Chairman of the Management Committee of the COST B11 action "Quantitation of Magnetic Resonance Image Texture"

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Texture Analysis Methods - A Review

Many texture-segmentation schemes use an elaborate bank of filters to decompose a textured image into a joint space/spatial-frequency representation. Although these schemes show promise, and although some analytical work has been done, the relationship between texture differences and the filter configurations required to distinguish them remain largely unknown. This paper examines the issue of designing individual filters. Using a 2-D texture model, we show analytically that applying a properly configured bandpass filter to a textured image produces distinct output discontinuities at texture boundaries; the analysis is based on Gabor elementary functions, but it is the bandpass nature of the filter that is essential. Depending on the type of texture difference, these discontinuities form one of four characteristic signatures: a step, ridge, valley, or a step change in average local output variation. Accompanying experimental evidence indicates that these signatures are useful for segmenting an image. The analysis indicates those texture characteristics that are responsible for each signature type. Detailed criteria are provided for designing filters that can produce quality output signatures. We also illustrate occasions when asymmetric filters are beneficial, an issue not previously addressed. >

Texture segmentation using 2-D Gabor elementary functions

The ability to segment a textured image into separate regions (texture segmentation) continues to be a challenging problem in computer vision. Many texture-segmentation schemes are based on a filter-bank model, where the filters (henceforth referred to as Gabor Filters) are derived from Gabor elementary functions. The goal of these methods is to transform texture differences into detectable filter-output discontinuities at texture boundaries. Then, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the parameters defining the Gabor filters are suitably chosen. Some previous analysis has shown how to design appropriate filters for discriminating simple textures. Designing filters for more general textures, though, has largely been done ad hoc. We have devised a new, more effective, more rigorously based method for determining Gabor-filter parameters. The method is based on an exhaustive, but efficient, search of Gabor-filter parameter space and on a detection-theory formulation of a Gabor filter''s output. We provide qualitative arguments and experimental results indicating that our new method is more effective than other methods in producing suitable filter parameters. We demonstrate that our model also gives good filter designs for a variety of texture types.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Determining Gabor-filter parameters for texture segmentation

Many texture-segmentation schemes use Gabor elementary functions as filters to decompose a textured image into a joint space/spatial-frequency domain. Dunn et al. (1991, 1992) showed for a 1-D model that these filters (when properly tuned) can produce certain characteristic discontinuities (step, valley and ridge) at texture boundaries, aiding image segmentation. The authors generalize the work of Dunn et al. by providing a more general 2-D analytical development. They also show when vertical and horizontal texture-phase discontinuities produce ridge or valley discontinuities, and when asymmetric filters are beneficial. >

2D analysis of Gabor-filter output signatures for texture segmentation

Many texture-segmentation mechanisms take the form of an elaborate bank of filters. Commonly used within such mechanisms are the Gabor elementary functions (GEFs). While filter-bank-based mechanisms show promise and some analytical work has been done to demonstrate the efficacy of GEFs, the relationships between texture differences and the filter configurations required to discriminate these differences remain largely unknown. In this paper, we given analytical and experimental evidence that suggests that various types of discontinuities can occur at texture boundaries when appropriately 'tuned' GEF-based filters are applied to a textured image; thus, by applying a discontinuity-detection scheme (i.e., edge detector) to such a filtered image, one can segment the image into different textured regions.

Joseph Wakeley

Papers

Texture segmentation using 2-D Gabor elementary functions

Determining Gabor-filter parameters for texture segmentation

2D analysis of Gabor-filter output signatures for texture segmentation

Texture boundary classification using Gabor elementary functions