Experiments in colour texture analysis

Drîmbarean, Alexandru; Whelan, Paul F.

doi:10.1016/s0167-8655(01)00058-7

Cited by 185 publications

(136 citation statements)

References 11 publications

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“…The detailed study on the researches in perceiving the images with human color vision [25] is done. For perceiving the images with human vision, they suggest to work on separated color spaces [3,6,30]. Even though the texture information provides effective feature, many researches [6,[10][11][12][13][14] show that combining other features along with it may give better results.…”

Section: Proposed Workmentioning

confidence: 99%

“…For perceiving the images with human vision, they suggest to work on separated color spaces [3,6,30]. Even though the texture information provides effective feature, many researches [6,[10][11][12][13][14] show that combining other features along with it may give better results. At the conclusion of these studies to incorporate both information about color and texture and to exhibit the human color vision, local texture feature is combined with color feature in opponent color space.…”

Section: Proposed Workmentioning

confidence: 99%

“…The odd rows of the code have the value g(i, j) whose values varies for different columns (columns-1,4,7,..., columns-2,5,8,..., columns-3,6,9,...) as u(i, j), v(i, j) and w(i, j) using Equations (5), (6) and (7) respectively and even rows of the code have the value h(i, j) , whose values varies for different columns (columns-1,4,7,..., columns-2,5,8,..., columns-3,6,9,...) as x(i, j), y(i, j) and z(i, j) using Equations (8), (9) and (10) respectively. …”

Section: Proposed Workmentioning

confidence: 99%

See 2 more Smart Citations

Tri-Chrominance Texture Pattern: A New Feature Descriptor

Jacob¹,

Srinivasagan²,

Gomathi³

et al. 2018

IJET

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Feature extraction plays a vital role in the information management system. This paper proposes Tri-Chrominance Texture Pattern (TCTP), a feature descriptor for extracting the features from images. This pattern helps to extract the inter-channel chrominance relationship, along with texture information of the image. The analysis were done in a natural image dataset, Corel database (DB1), pure colored texture database, Colored Brodaz Texture database (DB2) and a biometric dataset, Indian Face Image database (DB3). The proposed work outperforms the existing works in all the datasets. The analysis on DB1 shows significant improvement over the previous works

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Section: Proposed Workmentioning

confidence: 99%

Section: Proposed Workmentioning

confidence: 99%

Section: Proposed Workmentioning

confidence: 99%

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Tri-Chrominance Texture Pattern: A New Feature Descriptor

Jacob¹,

Srinivasagan²,

Gomathi³

et al. 2018

IJET

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“…Most textures have a colour aspect and most colour surfaces are textured. A colour texture can be regarded as a pattern described by the relationship between its chromatic and structural distribution [1]. Segmentation of colour texture images involves identifying the boundary maps between various uniform regions.…”

Section: Introductionmentioning

confidence: 99%

“…This performs a scene segmentation based on colour and texture information. Drimbarean and Whelan [1] examined the contribution of colour information to the overall classification performance. They extended the grey level algorithms to colour images and found that the inclusion of colour increases the classification results without significantly complicating the feature extraction algorithms.…”

Section: Introductionmentioning

confidence: 99%

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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