Unsupervised texture segmentation using feature distributions

Ojala, Timo; Pietikäinen, Matti

doi:10.1007/3-540-63507-6_216

Cited by 87 publications

(149 citation statements)

References 13 publications

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“…On the other hand, DCT is combined with the chrominance features for colour texture segmentation. A dissimilarity measure, G-Statistic [8], is used for colour texture discrimination. In this paper an unsupervised texture segmentation method developed by Ojala et al [8] is used.…”

Section: Colour Texture Segmentationmentioning

confidence: 99%

“…A dissimilarity measure, G-Statistic [8], is used for colour texture discrimination. In this paper an unsupervised texture segmentation method developed by Ojala et al [8] is used. In unsupervised texture segmentation, statistical analysis is per- Fig.…”

Section: Colour Texture Segmentationmentioning

confidence: 99%

“…This method follows hierarchical splitting and agglomerative merging procedure. The hierarchical splitting divides the image into blocks of roughly uniform texture and agglomerative merging procedure merges the similar adjacent regions until a stopping rule is satisfied [8]. We proposed a novel data structure to implement the algorithm for unsupervised texture segmentation [12].…”

Section: Colour Texture Segmentationmentioning

confidence: 99%

“…LBP and the DCT developed by Ojala et al [6] and Ng et al [7] respectively are used as the feature extraction techniques to extract features from the intensity plane, followed by the extraction of colour features from the chrominance planes. An unsupervised texture segmentation method [8] is used to segment the image. A pixelwise classification is pursued to enhance the segmented result on the boundaries.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis

2003

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Abstract. This paper describes a method for colour texture analysis, which performs segmentation based on colour and texture information. The main goal of this approach is to examine the contribution of chromaticity features in the analysis of texture. Local binary pattern and discrete cosine transform are the techniques utilised as a tool to perform feature extraction. Segmentation is carried out based on an unsupervised texture segmentation method. The performance of the method is evaluated using different chromaticity features and also using the ROC curves. The results indicate that the inclusion of colour information improves the segmentation performance.

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Section: Colour Texture Segmentationmentioning

confidence: 99%

Section: Colour Texture Segmentationmentioning

confidence: 99%

Section: Colour Texture Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis

2003

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“…Jain et al [1] filtered images using a bank of Gabor filters, utilized a Gaussian window to measure texture features in the filtered image, and adopted a square-error clustering algorithm to integrate texture features and make segmentations. Ojala et al [2] utilized distributions of local binary patterns and pattern contrast to evaluate the similarity of neighboring image regions using G statistics to compare distributions. Will et al [3] defined textures through the statistical distribution of Gabor filters responses, and the edges of textures were determined as pixels with equal posteriori probability between two classes of textures.…”

Section: Introductionmentioning

confidence: 99%

A simplified texture gradient method for improved image segmentation

Wang

Spratling

2015

SIViP

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Inspired by the Probability of boundary (Pb) algorithm, a simplified texture gradient method has been developed to locate texture boundaries within gray-scale images. Despite considerable simplification, the proposed algorithm's ability to locate texture boundaries is comparable with Pb's textureboundary method. The proposed texture gradient method is also integrated with a biologicallyinspired model, to enable boundaries defined by discontinuities in both intensity and texture to be located. The combined algorithm outperforms the current state-of-art image segmentation method (Pb) when this method is also restricted to using only local cues of intensity and texture at a single-scale.

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Spatial‐spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images

Gowen

2019

Journal of Chemometrics

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This work proposes a new method to treat spatial and spectral information interactively. The method extracts spatial features, ie, variogram, gray-level co-occurrence matrix (GLCM), histograms of oriented gradients (HOG), and local binary pattern (LBP) features, from each wavelength image of hypercube and principal component analysis (PCA) is applied on this spatial feature matrix to identify wavelength-dependent variation in spatial patterns. Resultant image is obtained by projecting the score values to the original data. Three datasets, including a synthetic hyperspectral image (Dataset 1), a set of real hyperspectral images of salmon fillets (Dataset 2), and remote-sensing images (Dataset 3), were utilized to evaluate the performance of the proposed method.Results from Dataset 1 showed that the spatial-spectral methods had the potential of reducing baseline offset noise. Dataset 2 revealed that spatial-spectral methods can alleviate noisy pixels with strong signal and reduce shadow effects. In addition, substantial improvements were obtained in case of classification between white stripe and red muscle pixels by using the HOG-based approach with correct classification rate (CCR) of 0.97 compared with the models directly built from raw and standard normal variate (SNV) preprocessed spectra (CCR = 0.94). Samson image of Dataset 3 suggested the flexibility and effectiveness of the proposed method by improving CCR of 0.96 using conventional PCA on SNV pretreated spectra to 0.98 using GLCMbased approach on SNV preprocessed spectra. Overall, experimental results demonstrated that the spatial-spectral methods can improve the results found by using the spectral information alone because of the spatial information provided.

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Unsupervised texture segmentation using feature distributions

Cited by 87 publications

References 13 publications

Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis

Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis

A simplified texture gradient method for improved image segmentation

Spatial‐spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images

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