Unsupervised Segmentation Of Textured Images Research Articles

ウェーブレットフレーム分解におけるGMRFモデルを用いたテクスチャ画像の教師なし領域分割

ウェーブレットフレーム分解におけるGMRFモデルを用いたテクスチャ画像の教師なし領域分割

Texture Image Segmentation Based on MRMRF in Contourlet Domain

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

Unsupervised texture-based image segmentation through pattern discovery

This paper presents a new efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating texture patterns. First, a pattern discovery stage that relies on a clustering algorithm is utilized for determining the texture patterns of a given image based on the outcome of a multichannel Gabor filter bank. Then, a supervised pixel-based classifier trained with the feature vectors associated with those patterns is used to classify every image pixel into one of the sought texture classes, thus yielding the final segmentation. Multi-sized evaluation windows following a top-down approach are utilized during pixel classification in order to improve accuracy both inside and near boundaries of regions of homogeneous texture. Results with synthetic compositions and with complex real images are presented and discussed. The proposed technique is also compared with alternative texture segmentation approaches.

A Conditional Random Field Approach to Unsupervised Texture Image Segmentation

An unsupervised multiresolution conditional random field (CRF) approach to texture segmentation problems is introduced. This approach involves local and long-range information in the CRF neighbourhood to determine the classes of image blocks. Likemost Markov random field (MRF) approaches, the proposed method treats the image as an array of random variables and attempts to assign an optimal class label to each. While most MRFs involve only local information extracted from a small neighbourhood, our method also allows a few long-range blocks to be involved in the labelling process. This alleviates the problem of assigning different class labels to disjoint regions of the same texture and oversegmentation due to the lack of long-range interaction among the neighbouring and distant blocks. The proposed method requires no a priori knowledge of the number and types of regions/textures.

An adaptive region based color texture segmentation using fuzzified distance metric

Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, which efficiently embody the information about the textural characteristics of the image. This can be used for the segmentation of different texture images. Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. This paper, mainly focus on the unsupervised segmentation of color textured images as a prototypical application in computer vision. Here the objective is to group pixels or small image patches such that meaningful regions of identical/similar texture are obtained. This paper presents a new approach for color texture segmentation using Haralick features extracted from color co-occurrence matrices. The originality of this approach is to select the most discriminating color texture features extracted from the color co-occurrence matrices calculated in HSI color space. Fuzzified distance metric is used for achieving texture segmentation.

Unsupervised texture image segmentation using multilayer data condensation spectral clustering

A novel unsupervised texture image segmentation using a multilayer data condensation spectral clustering algorithm is presented. First, the texture features of each image pixel are extracted by the stationary wavelet transform and a multilayer data condensation method is performed on this texture features data set to obtain a condensation subset. Second, the spectral clustering algorithm based on the manifold similarity measure is used to cluster the condensation subset. Finally, according to the clustering result of the condensation subset, the nearest-neighbor method is adopted to obtain the original image-segmentation result. In the experiments, we apply our method to solve the texture and synthetic aperture radar image segmentation and take self-tuning k-nearest-neighbor spectral clustering and Nyström methods for baseline comparisons. The experimental results show that the proposed method is more robust and effective for texture image segmentation.

Unsupervised textured image segmentation using 2-D quarter plane autoregressive model with four prediction supports

In the context of the model-based methods for image processing, we propose some improvements for an unsupervised textured image segmentation algorithm using a 2-D quarter plane autoregressive model. The segmentation algorithm works in two stages: The first stage consists in an estimation of both the number of textures and the model parameters associated with each existing texture. The estimation is achieved by minimizing a probabilistic criterion which comprises a penalty term such as those used in information criteria (IC). The second stage deals with a maximum a posteriori estimation of the label field by a simulated annealing method. In a former work, Akaike IC (AIC) and a 2-D first quarter plane autoregressive model with fixed (1, 1) order were used. In order to estimate the number of textures and the model orders, we propose to use Bayesian IC (BIC) and ϕ β IC. Moreover, during the two stages of the algorithm, the four quarter planes prediction supports have been used in order to solve problems at image and region boundaries. The results are given on images containing synthetic and natural textures.

Unsupervised Segmentation of Textured Color Images Using Fuzzy Homogeneity Decision

This paper proposes a fuzzy-based unsupervised segmentation of textured color images. L*a*b* color space is used to represent color features and statistical geometrical features (SGF) are adopted as texture descriptors. Homogeneity decision is used to make a fusion of texture features and color features with fuzzy-rule theory. Hierarchical segmentation based on the fuzzy homogeneity decision is performed in four processes: hierarchical splitting, local agglomerative merging, global agglomerative merging and pixelwise classification. Experiments on segmentation of some color texture mosaics and color natural images are presented to verify the effectiveness of the proposed segmentation approach.

MRF-based texture segmentation using wavelet decomposed images

In recent textured image segmentation, Bayesian approaches capitalizing on computational efficiency of multiresolution representations have received much attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid image decomposition. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm based on a multiscale stochastic modeling over the wavelet decomposition of image. The model, using doubly stochastic Markov random fields, captures intrascale statistical dependencies over the wavelet decomposed image and intrascale and interscale dependencies over the corresponding multiresolution region image.

Scale-dependent hierarchical unsupervised segmentation of textured images

This paper presents a new three-staged texture-based segmentation method that, using multiresolution techniques, provides high computational speed. The main novelty of the approach is that spatial proximity has a progressively decreasing importance on lower resolution levels when hierarchically segmenting the data structure. The method achieves low error rates and it does not require any knowledge about the number of textures in the image.

Histogram clustering for unsupervised segmentation and image retrieval

This paper introduces a novel statistical latent class model for probabilistic grouping of distributional and histogram data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical application of this method for unsupervised segmentation of textured images based on local distributions of Gabor coefficients. Benchmark results indicate superior performance compared to K-means clustering and proximity-based algorithms. In a second application the histogram clustering method is utilized to structure image databases for improved image retrieval.

Unsupervised segmentation of textured images

In this paper, a new unsupervised segmentation algorithm for textured images is developed. The proposed algorithm utilizes the characteristic of the Markov random fields (MRF) for modeling the contextual information embedded in image formation. Textured images are regarded as realizations of the stationary Gaussian MRF on a two-dimensional square lattice, and are modeled by the conditional autoregressive (CAR) equations with a second-order noncausal neighborhood. A hypothesis test over two radially masked areas is involved in our algorithm to detect the location of boundaries. The hypothesis assumes that these two areas belong to the same class of textures, and CAR model parameters are estimated in a minimum-mean-square-error (MMSE) sense. The hypothesis is tested by the analysis of variance (ANOVA)-like technique, and if it is rejected, a measure of dissimilarity is accumulated on the rejected area. This approach produces potential edge maps. From these maps, the boundaries among various textures can be detected without microedges. The performance of the proposed algorithm is demonstrated by some experiments using real textures as well as synthetic ones. The experiments show that the proposed algorithm can produce satisfactory segmentation without any a priori information.

Markov random field models for unsupervised segmentation of textured color images

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation. >

Unsupervised segmentation of textured images using a hierarchical neural structure

A hierarchical learning structure, combining a randomly-placed local window, a self-organising map and a local-voting scheme, has been developed for the unsupervised segmentation of textured images, which are modelled by Markov random fields. The system learns to progressively estimate model parameters, and hence classify the various textured regions. A globally correct segregation has consistently been obtained during extensive experiments on both synthetic and natural textured images.

Textured image segmentation by context enhanced clustering

An unsupervised textured image segmentation technique based on multidimensional feature vector clustering is described, where the features are the parameters of an autoregressive model, The benefits of incorporating spatial contextual information are demonstrated on both true cluster number estimation and actual image segmentation. A simple within-cluster distance is used for cluster validity analysis, where feature vectors are modified through local spatial dependency. This greatly reduces the dispersion in the raw feature data fed to the clustering process, and improves the true cluster number estimation. At the segmentation stage, three schemes incorporating contextual information at feature vector and label levels are proposed to enhance the segmentation accuracy. One is a development of a technique due to Mardia and Hainsworth (1988). The proposed approaches are tested on a four-class textured image.

Unsupervised segmentation of noisy and textured images using Markov random fields

This paper proposes a general unsupervised segmentation algorithm which estimates all model parameters, including the number of regions, as part of the segmentation. The general image model is a hierarchical one, consisting of Markov random fields as components of the model. The MAP criterion is adopted, in principle, for the simultaneous image segmentation and parameter estimation procedures. Due to the difficulty of implementing the MAP segmentation with a large number of unknown parameters, a novel modification of the MAP criterion is proposed. For the model parameters having closed-form ML estimates, these estimates are substituted back into the objective function to reduce the difficulty of the maximization. The remaining maximization is implemented by a recursive segmentation-parameter estimation algorithm, which yields a partial optimal solution (POS) to the maximization problem. In the special case where all model parameters have closed-form ML estimates, the proposed algorithm is equivalent to implementing the MAP criterion. The number of regions in the image is determined through a model fitting criterion tagged on to the segmentation algorithm. Special forms of the general unsupervised segmentation algorithm are developed for the segmentation of noisy and textured images. For noisy images, the image is assumed to consist of uniform graylevel regions modeled by a class of Gibbs random fields and corrupted by additive, white, region-dependent, Gaussian noise. For textured images, the image is assumed to consist of regions, modeled by a class of Gibbs random fields, which are filled with textures, modeled by Gaussian Markov random fields. The algorithms for both classes of images are applied to a wide range of images—generated according to the model, hand-drawn, natural and Brodatz textures, their combinations, and outdoor images—with notable success. Despite the large number of unknown parameters (as many as 14 for some noisy images and 36 for some textured images), the algorithms yield good segmentations, accurate estimates for the parameters, and the correct number of regions.

Maximum likelihood unsupervised textured image segmentation

This paper presents an algorithm for segmenting an image that is composed of an unknown number of regions c. In each region n, the image data g n are viewed as a realization from a homogeneous parametric random field with a class conditional density function p(g n | γ n ), where γ n is an unknown parameter set. The number of regions c and the segmentation S c are treated as unknown constants that are estimated using the maximum likelihood (ML) estimation principle. The ML estimates for c and S c are obtained by maximizing log{ p(g|S c)} over all possible c and S c . p(g|S c) has the desirable property of unbiasedness; i.e., E S ctrue {log{ p(g|S c )}} ≤ E S ctrue {log{ p(g|S ctrue )}. Unfortunately, it suffers from two limitations: (i) a closed-form analytic expression for p(g|S c ) for a given fixed c cannot be obtained in general, and (ii) in order to arrive at the optimum ( c ∗, S c ∗ ) we must evaluate p(g|S c) for all possible c and S c , a most formidable task. This paper presents a solution to both problems that results into an optimum number of classes c ∗ ; an “optimum” window-based coarse segmentation S c ∗ of the image; and a ML estimate of the parameters Γ S ∗c ∗ = (γ 1, γ 2, …, γ c ∗ ) of the c ∗ regions induced by S c ∗ . From this knowledge, the mixed windows (windows that fall between regions) are segmented further in a supervised mode (known parameter case) using the ML high-resolution segmentation developed by Cohen and Cooper ( IEEE Trans. Pattern Anal. Mach. Intelligence Mar., 1987). The ML algorithm is applied to the problem of unsupervised segmentation of textured images of natural outdoor scenes.

Unsupervised texture segmentation using Markov random field models

The problem of unsupervised segmentation of textured images is considered. The only explicit assumption made is that the intensity data can be modeled by a Gauss Markov random field (GMRF). The image is divided into a number of nonoverlapping regions and the GMRF parameters are computed from each of these regions. A simple clustering method is used to merge these regions. The parameters of the model estimated from the clustered segments are then used in two different schemes, one being all approximation to the maximum a posterior estimate of the labels and the other minimizing the percentage misclassification error. The proposed approach is contrasted with the algorithm of S. Lakshamanan and H. Derin (1989), which uses a simultaneous parameter estimation and segmentation scheme. The results of the adaptive segmentation algorithm of Lakshamanan and Derin are compared with a simple nearest-neighbor classification scheme to show that if enough information is available, simple techniques could be used as alternatives to computationally expensive schemes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Unsupervised textured image segmentation using feature smoothing and probabilistic relaxation techniques

This paper describes an unsupervised textured image segmentation algorithm. The goals are to improve textured image segmentation results, especially along the borders of regions; and to take into account the spatial relationship among pixels to improve the segmentation of region interiors. An improved method to extract textured energy features in the feature extraction stage is described. The method is based upon an adaptive noise smoothing concept which takes the nonstationary nature of the problem into account. Texture energy features are first estimated using a window of small size to reduce the possibility of mixing statistics along region borders. The estimated texture energy feature values are then smoothed by a quadrant filtering method to reduce the variability of the estimates while retaining the region border accuracy. In this unsupervised segmentation system, the estimated feature values are used in a K-means clustering algorithm to estimate the class statistics. The estimated class statistics are then used by the Bayes classifier to make an initial probabilistic labeling. The spatial constraints are then enforced through the use of a probabilistic relaxation algorithm. Limiting the probability labels by probability thresholding is used to make the relaxation iteration more efficient. The trade-off between efficiency and degradation of performance is studied. Finally, an overview of the proposed textured image segmentation system is provided and comparisons of overall performance are made.

Multiresolution feature extraction and selection for texture segmentation

An approach is described for unsupervised segmentation of textured images. Local texture properties are extracted using local linear transforms that have been optimized for maximal texture discrimination. Local statistics (texture energy measures) are estimated at the output of an equivalent filter bank by means of a nonlinear transformation (absolute value) followed by an iterative Gaussian smoothing algorithm. This procedure generates a multiresolution sequence of feature planes with a half-octave scale progression. A feature reduction technique is then applied to the data and is determined by simultaneously diagonalizing scatter matrices evaluated at two different spatial resolutions. This approach provides a good approximation of R.A. Fisher's (1950) multiple linear discriminants and has the advantage of requiring no a priori knowledge. This feature reduction methods appears to be an improvement on the commonly used Karhunen-Loeve transform and allows efficient texture segmentation based on simple thresholding. >