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.

The design of filters for texture feature extraction is addressed. Based on a new feature extraction model, optimization approaches utilizing various feature (energy) separation criteria are developed. Both two- and multiple-texture problems are addressed. The approaches are assessed by supervised segmentation experiments. The experiments also include results from alternative filter optimization approaches.

A new scheme for color and texture feature extraction for image content search is presented. We introduce a scheme using the two chrominance components for color information and a computationally efficient infinite impulse response (IIR) quadrature mirror filter bank (QMF) energy measure of the luminance component for texture information. The color and texture information is combined into one feature vector, and the components are balanced with respect to dimensionality. We illustrate the utility of our features with experiments in searching for a specific color texture in a large database of images. Several different sub-band decompositions are evaluated. Features extracted using a previously published Gabor filter bank are also evaluated against the proposed scheme. We conclude that the proposed scheme outperforms the Gabor features in both quality and complexity.

An important category of texture features are features extracted by filters together with a filter response energy measure. Most approaches to texture filtering use filter banks which are selected by some heuristic criteria and which are not optimal with respect to the given data. Some approaches to optimal filter design have been presented. A few approaches to multi-texture optimal filter bank design have also been presented, but they are all restricted to very small sets of free parameters. In this paper a new technique for multiple texture optimal design of a bank of general finite impulse response (FIR) filters is presented. A closed form optimal solution is obtained by modeling the feature extraction for the textures.

This paper deals with the design of a finite impulse response filter for optimal feature extraction in texture segmentation/classification. Motivated by the Fisher criterion, we develop techniques for optimal filter design. Posing some restrictions on the data, we are able to find a closed form solution to the problem, and we develop a unifying framework for this and several other approaches. We also show how the optimal filters may be designed, using less restrictive assumptions and a gradient search.

Several approaches to multichannel filtering for texture classification and segmentation with Gabor filters have been proposed. The rationale presented for the use of the Gabor filters is their relation to models for the early vision of mammals as well as their joint optimum resolution in time and frequency. In this work we present a critical evaluation of the Gabor filters as opposed to filter banks used in image coding -- in both full rate and critically sampled realizations. In the critically sampled case, tremendous computational savings can be realized. We further evaluate the commonly used octave band decomposition versus alternative decompositions. We conclude that for a texture segmentation task it is possible to use a wider range of filters than just the Gabor class of filters, it is possible to use alternative decompositions and, most important, it is possible to use subsampled filters.