Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis

2003 ◽  
Vol 90 (2) ◽  
pp. 190-216 ◽  
Author(s):  
Nicolas Vandenbroucke ◽  
Ludovic Macaire ◽  
Jack-Gérard Postaire
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Color Space ◽  
Color Image Segmentation ◽  
Space Application ◽  
Pixel Classification

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

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Dina Khattab ◽  
Hala Mousher Ebied ◽  
Ashraf Saad Hussein ◽  
Mohamed Fahmy Tolba
Keyword(s):  
Image Segmentation ◽  
Comparative Study ◽  
Color Image ◽  
Color Space ◽  
User Interaction ◽  
Color Image Segmentation ◽  
Space Representation ◽  
Color Spaces ◽  
Different Color ◽  
The Comparative Study

This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied withRGB,HSV,CMY,XYZ, andYUVcolor spaces. The comparative study and experimental results using different color images show thatRGBcolor space is the best color space representation for the set of the images used.

The Study of Forest Fire Color Image Segmentation

2011 ◽  
Vol 474-476 ◽  
pp. 2140-2145
Author(s):  
Si Li ◽  
Hong E Ren
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Color Image ◽  
Color Space ◽  
Color Images ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Segmentation Methods ◽  
Time Periods ◽  
Night And Day

Combined with the composition characteristics of forest fire image background when the forest fire occurred during different time periods of night and day, different image segmentation methods were applied to the forest fire color images of different time periods respectively, which could improve the efficiency of image processing. Meanwhile, application of H and S components from HSV color space, the strategy on color image segmentation which processed the segmentation processing to forest fire color images with complicated background was proposed combined with Otsu algorithm. The results of simulation experiment showed that the above-mentioned segmentation methods were obtained satisfactory segmentation effects when the segmentation on forest fire color images during different time periods of night and day were processed respectively. And also application of Otsu algorithm based on HSV color model, the forest fire image segmentation occurred in the daytime was processed, which overcame the interference factors of light and smoke, as well as the shortage of noise sensibility due to Otsu algorithm.

scholarly journals Human Perception Based Color Image Segmentation

2013 ◽  
Vol 2 (3) ◽  
Author(s):  
Neeta Pradeep Gargote ◽  
Savitha Devaraj ◽  
Shravani Shahapure
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Color Image ◽  
Color Space ◽  
Human Perception ◽  
Activation Function ◽  
Color Image Segmentation ◽  
Neural Network Architecture ◽  
Hsi Color Space

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images.

scholarly journals A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances

2021 ◽  
Vol 7 (10) ◽  
pp. 208
Author(s):  
Giacomo Aletti ◽  
Alessandro Benfenati ◽  
Giovanni Naldi
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Random Walk ◽  
Dirichlet Problem ◽  
Color Image ◽  
Color Image Segmentation ◽  
Computational Time ◽  
Random Walker ◽  
Segmentation Process ◽  
Colour Distance

Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8–neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and k−means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.

Color Image Segmentation

2012 ◽  
Vol 3 (3) ◽  
pp. 66-82
Author(s):  
Song Gao ◽  
Chengcui Zhang ◽  
Wei-Bang Chen
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Space ◽  
Feature Space ◽  
Color Image Segmentation ◽  
Hill Climbing ◽  
Region Detection ◽  
Projective Clustering

An intuitive way of color image segmentation is through clustering in which each pixel in an image is treated as a data point in the feature space. A feature space is effective if it can provide high distinguishability among objects in images. Typically, in the preprocessing phase, various modalities or feature spaces are considered, such as color, texture, intensity, and spatial information. Feature selection or reduction can also be understood as transforming the original feature space into a more distinguishable space (or subspaces) for distinguishing different content in an image. Most clustering-based image segmentation algorithms work in the full feature space while considering the tradeoff between efficiency and effectiveness. The authors’ observation indicates that often time objects in images can be simply detected by applying clustering algorithms in subspaces. In this paper, they propose an image segmentation framework, named Hill-Climbing based Projective Clustering (HCPC), which utilizes EPCH (an efficient projective clustering technique by histogram construction) as the core framework and Hill-Climbing K-means (HC) for dense region detection, and thereby being able to distinguish image contents within subspaces of a given feature space. Moreover, a new feature space, named HSVrVgVb, is also explored which is derived from Hue, Saturation, and Value (HSV) color space. The scalability of the proposed algorithm is linear to the dimensionality of the feature space, and our segmentation results outperform that of HC and other projective clustering-based algorithms.

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