Quaternion color texture segmentation

Shi, Lilong; Funt, Brian V.

doi:10.1016/j.cviu.2006.11.014

Cited by 79 publications

(43 citation statements)

References 12 publications

(27 reference statements)

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“…A conceptually related feature integration approach has been explored in the paper by Shi and Funt [28]. The major idea behind this approach is to provide a compact representation where the components of the RGB colour space are converted into a quaternion form that can be written as q¼R Á iþG Á jþB Á k. The proposed algorithm consists of three computational stages.…”

Section: Implicit Colour-texture Integrationmentioning

confidence: 99%

“…(1) Implicit colour-texture feature integration [24][25][26][27][28][29][30]44]: the algorithms that belong to this category extract the texture features from single or multiple colour channels and the segmentation process is typically embedded into a coarseto-fine strategy. (2) Approaches that extract colour and texture in succession, i.e.…”

Section: Colour-texture Segmentation: Main Directions Of Researchmentioning

confidence: 99%

See 1 more Smart Citation

Image segmentation based on the integration of colour–texture descriptors—A review

Ilea

Whelan

2011

Pattern Recognition

182

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Section: Implicit Colour-texture Integrationmentioning

confidence: 99%

Section: Colour-texture Segmentation: Main Directions Of Researchmentioning

confidence: 99%

Image segmentation based on the integration of colour–texture descriptors—A review

Ilea

Whelan

2011

Pattern Recognition

182

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“…In 2011, Subakan and Vemuri presented feature preserving models for color image smoothing and segmentation [3]. In 2007, Shi and Funt extended the PCA based on quaternion for color texture segmentation [16].…”

Section: H H H H H Hmentioning

confidence: 99%

A quaternion-based spectral clustering method for color image segmentation

Jin

Liu

et al. 2011

SPIE Proceedings

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Spectral clustering method has been widely used in image segmentation. A key issue in spectral clustering is how to build the affinity matrix. When it is applied to color image segmentation, most of the existing methods either use Euclidean metric to define the affinity matrix, or first converting color-images into gray-level images and then use the gray-level images to construct the affinity matrix (component-wise method). However, it is known that Euclidean distances can not represent the color differences well and the component-wise method does not consider the correlation between color channels. In this paper, we propose a new method to produce the affinity matrix, in which the color images are first represented in quaternion form and then the similarities between color pixels are measured by quaternion rotation (QR) mechanism. The experimental results show the superiority of the new method.

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“…However, Bülow does not apply the quaternionic Gabor filters to color or vector-valued images. Lilong Shi and Brian Funt in [5] propose a colour texture segmentation method that makes possible encoding the structural and colour feature as a whole unit by using the quaternion represented colour and use quaternion principal component analysis(QPCA) with colours encoded as quaternions to calculate a basis for colour texture. Our approach to colour texture segmentation is directly based on Bülow's method.…”

Section: Related Workmentioning

confidence: 99%