Unsupervised texture segmentation using Gabor filters
TL;DR: A texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system is presented, which is based on reconstruction of the input image from the filtered images.
About: This article is published in Pattern Recognition.The article was published on 1991-12-01 and is currently open access. It has received 2351 citations till now. The article focuses on the topics: Image texture & Texture filtering.
Summary (2 min read)
Jump to: [Introduction] – [2 Channel Characterization] – [2.1 Choice of Filter Parameters] – [Z,Y] – [4 Integrating Feature Images] – [5 Experimental Results] and [6 Summary and Conclusions]
Introduction
- Image segmentation is a difficult yet very important task in many image analysis or computer vision applications.
- A large number of techniques for analyzing image texture has been proposed in the past two decades [ll, 221.
- This approach is inspired by a multi-channel filtering theory for processing visual information in the early stages of the human visual system.
- The psychophysical experiments that suggested such a decomposition used various grating pattems as stimuli and were based on adaptation techniques [4] .
- 'This work was supported in part by the National Science Foundation infrastructure grant CDA-8806599, and by a grant from E. I.
2 Channel Characterization
- The use of Gabor filters in texture analysis is not new.
- Tumer scrated their potential for texture discrimination.
- Similarly, Perry & Lothe authors 1191 use a fixed set of Gabor filters in their texture segmentation algorithm.
- Instead of using a fixed set of filters, Bovik er al. apply a simple peak finding algorithm to the power spectrum of the image in order to determine the radial frequencies of the appropriate Gabor filters.
2.1 Choice of Filter Parameters
- Figure 4 shows examples of filtered images for an image containing 'straw matting' @55) and 'wood grain' (D68) textures from the photographic album of textures by Brodatz [2] .
- The ability of the filters to exploit differences in spatial-frequency (size) and orientation in the two textures is evident in these images.
- The differences in the strength of the responses in regions with different textures is the key to the multi-channel approach to texture analysis.
- To maximize visibility, each filtered image has been scaled to full contrast.
Z,Y
- Note that s(z,y) has a mean of zero, since the mean gray value of each filtered image is zero.
- For computational efficiency, the authors determine the "best" subset 01 .he filtered images by the following suboptimal sequential forward selection procedure: Let r;(z,y) be the zth filtered image and R,(u,v) be its Discrete Fourier Transform.
- The amount of overlap between the MTFs of the Gabor filters in their filter set is small.
- These energies can be computed in the Fourier domain, hence avoiding unnecessary inverse Fourier transforms.
4 Integrating Feature Images
- In texture segmentation, neighboring pixels are very likely to belong to the same texture category.
- The authors propose a simple method that incorporates the spatial adjacency information directly in the clustering process.
- This is achieved by including the spatial coordinates of the pixels as two additional features.
5 Experimental Results
- The lack of appropriate quantitative measures of the goodness of a segmentation makes it very difficult to evaluate and compare texture segmentation algorithms.
- One simple criterion that is often used is the percentage of misclassified pixels.
- Table 1 gives the percentage of misclassified pixels for the segmentation experiments reported here.
6 Summary and Conclusions
- In their texture segmentation algorithm, the authors assumed that the number of texture categories is given.
- The authors believe that an integrated approach that uses both a region-based and an edge-based segmentation can be used to resolve the question of determining the number of texture categories.
- Mal& 8~ Perona [16], for example, have developed a multi-channel filtering technique that produces edge-based segmentations.
- The basic idea is to generate an oversegmented solu-19 tion using their region-based texture segmentanorl algorithm.
- This integrated approach is currently being investigated.
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References
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Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >
20,028 citations
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TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
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