Maximum likelihood unsupervised textured image segmentation

Cohen, Fernand S.; Fan, Zhigang

doi:10.1016/1049-9652(92)90054-2

Cited by 37 publications

(22 citation statements)

References 9 publications

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“…Early methods proposed for unsupervised region-based texture segmentation include approaches based on split-and-merge methods [5], pyramid node linking [6], selective feature smoothing with clustering [7], and a quadtree method combining statistical and spatial information [8]. Examples of more recent approaches are methods based on local linear transforms and multiresolution feature extraction [9], feature smoothing and probabilistic relaxation [10], autoregressive models [11,12], Markov random field models [13][14][15][16], multichannel filtering [17][18][19], neural network-based generalization of the multichannel approach [20], wavelets [21,22], fractal dimension [23], and hidden Markov models [24]. A method for unsupervised segmentation of color textures using Markov random fields and a split-and-merge type algorithm was proposed by Panjwani and Healey [25].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised texture segmentation using feature distributions

Ojala

Pietikäinen

1999

Pattern Recognition

309

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Section: Introductionmentioning

confidence: 99%

Unsupervised texture segmentation using feature distributions

Ojala

Pietikäinen

1999

Pattern Recognition

309

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“…The contours move in the normal direction with a speed of g(I(x, y))(κ(φ(x, y)) + ν), and therefore stops on the edges, where g(·) vanishes. The curvature term κ(·) maintains the regularity of the contours, while the constant term ν accelerates and keeps the contour evolution by minimizing the enclosed area [81].…”

Section: Edge-based Active Contoursmentioning

confidence: 99%

Active Contours for Multispectral Images With Non-Homogeneous Sub-Regions

Snyder¹

2005

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Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges.A partial solution to the problem of internal edges is to partition an image based on the statistical information of image intensity measured within sub-regions instead of looking for edges. Although representing an image as a piecewise-constant or unimodal probability density functions produces better results than traditional edge-based methods, the performances of such methods is still poor on images with sub-regions consisting of multiple components, e.g. a military vehicle covered by a camouflage pattern. The segmentation of this kind of multispectral images is even a more difficult problem. The object of this work is to develop advanced segmentation methods which provide robust performance on the images with non-uniform subregions.In this work, we propose a framework for image segmentation which partitions an image based on the statistics of image intensity where the statistical information is represented as a mixture of probability density functions defined in a multi-dimensional image intensity space. Depending on the method to estimate the mixture density functions, three active contour models are proposed: unsupervised multi-dimensional histogram method, half-supervised multivariate Gaussian mixture density method, and supervised multivariate Gaussian mixture density method. The implementation of active contours is done using level sets.The proposed active contour models show robust segmentation capabilities on images where traditional segmentation methods show poor performance. Also, the proposed methods provide a means of autonomous pattern classification by integrating image segmentation and statistical pattern classification.

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“…Traditionally, clustering in image segmentation is supervised or manually assisted by userspecifying the thresholds or the number of clusters [24], [25]. Several unsupervised image segmentation techniques have also been proposed, such as iterative dominance clustering [26], random field models [27], fuzzy clustering [28], and maximum likelihood [29]. Some of these techniques deal with less complex scenes and others with highly textured regions, and they are not readily extensible to complex satellite imagery.…”

Section: Methodsmentioning

confidence: 99%

“…The set of interpolated thresholds for a pixel is (28) such that (referring to Fig. 2) (29) such that , where is the four closest regions to the pixel. The points at the borders of the image are not surrounded by four regional centers.…”

Section: F Pointwise Interpolationmentioning

confidence: 99%

Segmentation of satellite imagery of natural scenes using data mining

Soh

Tsatsoulis

1999

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper, we describe a segmentation technique that integrates traditional image processing algorithms with techniques adapted from knowledge discovery in databases (KDD) and data mining to analyze and segment unstructured satellite images of natural scenes. We have divided our segmentation task into three major steps. First, an initial segmentation is achieved using dynamic local thresholding, producing a set of regions. Then, spectral, spatial, and textural features for each region are generated from the thresholded image. Finally, given these features as attributes, an unsupervised machine learning methodology called conceptual clustering is used to cluster the regions found in the image into N N N classes-thus, determining the number of classes in the image automatically. We have applied the technique successfully to ERS-1 synthetic aperture radar (SAR), Landsat thematic mapper (TM), and NOAA advanced very high resolution radiometer (AVHRR) data of natural scenes.

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Maximum likelihood unsupervised textured image segmentation

Cited by 37 publications

References 9 publications

Unsupervised texture segmentation using feature distributions

Unsupervised texture segmentation using feature distributions

Active Contours for Multispectral Images With Non-Homogeneous Sub-Regions

Segmentation of satellite imagery of natural scenes using data mining

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