Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

Khattab, Dina; Ebied, Hala M.; Hussein, Ashraf S.; Tolba, Mohamed F.

doi:10.1155/2014/126025

Cited by 62 publications

(35 citation statements)

References 22 publications

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“…These are some of the questions we address in this paper. Similar questions have been raised and answered for skin pixel segmentation [9][10][11], shadow and traffic object detection [12][13][14] and image segmentation by graph cut in [15,16]. Choice of color spaces influences object recognition see [17] for more details.…”

Section: Introductionmentioning

confidence: 86%

Analytical Study of Colour Spaces for Plant Pixel Detection

Kumar¹,

Miklavcic²

2017

Preprint

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Segmentation of a region of interest is an important pre-processing step for many colour image analysis techniques. Similarly segmentation of plant in digital images is an important preprocessing step in phenotying plants by image analysis. In this paper we present an analytical study to statistically determine the suitability of colour space representation of an image to best detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation in which the separation of the distributions representing plant pixels and background pixels is maximized would be the best for detection of plant pixels. The two classes of pixels are modelled as a Gaussian mixture model (GMM). In our GM modelling we don't make any prior assumption about the number of Gaussians in the model. Rather a constant bandwidth mean-shift filter is used to cluster the data and the number of clusters and hence the number of Gaussians is automatically determined. Here we have analysed following representative colour spaces like RGB, rgb, HSV, Ycbcr and CIE − Lab. This is because these colour spaces represent several other similar colour spaces and also an exhaustive study of all the colour space will be too voluminous. We also analyse the colour space feature from the two-class variance ratio perspective and compare the results of our hypothesis with this metric. The dataset for this empirical study consist of 378 digital images of plants and their manual segmentation. Dataset consist of various species of plants (arabidopsi, tobacco, wheat, rye grass etc.) imaged under different lighting conditions, indoor and outdoor, controlled and uncontrolled background. In results we obtain better segmentation of the plants in HSV colour space, which is supported by its Earth mover distance (EMD) on the GMM distribution of plant and background pixels.

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Section: Introductionmentioning

confidence: 86%

Analytical Study of Colour Spaces for Plant Pixel Detection

Kumar¹,

Miklavcic²

2017

Preprint

View full text Add to dashboard Cite

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“…These are some of the questions we address in this paper. Similar questions have been raised and answered for skin pixel segmentation [9][10][11], shadow and traffic object detection [12][13][14] and image segmentation by graph cut [15,16]. It has been shown in [17] that the choice of colour space does influence object recognition.…”

Section: Introductionmentioning

confidence: 86%

Analytical Study of Colour Spaces for Plant Pixel Detection

Kumar

Miklavcic

2018

J. Imaging

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Segmentation of regions of interest is an important pre-processing step in many colour image analysis procedures. Similarly, segmentation of plant objects in digital images is an important preprocessing step for effective phenotyping by image analysis. In this paper, we present results of a statistical analysis to establish the respective abilities of different colour space representations to detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation for which the separation of the distributions representing object and background pixels is maximized is the best for the detection of plant pixels. The two pixel classes are modelled by Gaussian Mixture Models (GMMs). In our statistical modelling we make no prior assumptions on the number of Gaussians employed. Instead, a constant bandwidth mean-shift filter is used to cluster the data with the number of clusters, and hence the number of Gaussians, being automatically determined. We have analysed the following representative colour spaces: RGB, rgb, HSV, Ycbcr and CIE-Lab. We have analysed the colour space features from a two-class variance ratio perspective and compared the results of our model with this metric. The dataset for our empirical study consisted of 378 digital images (and their manual segmentations) of a variety of plant species: Arabidopsis, tobacco, wheat, and rye grass, imaged under different lighting conditions, in either indoor or outdoor environments, and with either controlled or uncontrolled backgrounds. We have found that the best segmentation of plants is found using HSV colour space. This is supported by measures of Earth Mover Distance (EMD) of the GMM distributions of plant and background pixels.

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“…One of the disadvantages of GrabCut is that the segmentation is initialized by user interaction which may leads to bad segmentation if initialization quality is poor [11]. Rother [2] proposed further user editing to improve the segmentation but require more user interaction.…”

Section: Em Algorithm For Automatic Grabcutmentioning

confidence: 99%

“…Dina Khattab et al proposed Automatic GrabCut based on clustering technique [10],along with Automatic GrabCut using different color space model was tested [11] and multi-label Automatic GrabCut for image segmentation [12]. According to their data, though some samples' performance are not well, the average error rate and time consume are both lower than the original GrabCut [10].…”

Section: Introductionmentioning

confidence: 99%

Automatic GrabCut color Image Segmentation Based on EM Algorithm

Li¹,

Li²,

Qi³

2015

Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering

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Abstract. This paper presents a color image segmentation method using modified GrabCut. GrabCut is considered as one of semi-automatic image segmentation techniques, since it needs user interaction to initialize segmentation process. To eliminate this inconvenience, this paper propose using Expectation Maximization Algorithm for Gaussian mixture model to replace the original initialization process and achieve automatic GrabCut. After experiment and analysis, this paper gives an evaluation over speed and accuracy, proves practicability of this modification and provide a set up for further improvement.

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Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

Cited by 62 publications

References 22 publications

Analytical Study of Colour Spaces for Plant Pixel Detection

Analytical Study of Colour Spaces for Plant Pixel Detection

Analytical Study of Colour Spaces for Plant Pixel Detection

Automatic GrabCut color Image Segmentation Based on EM Algorithm

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