Color image segmentation by supervised pixel classification in a color texture feature space. Application to soccer image segmentation

2002 ◽  
Author(s):  
N. Vandenbroucke ◽  
L. Macaire ◽  
J.-G. Postaire
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Texture Feature ◽  
Feature Space ◽  
Color Image Segmentation ◽  
Space Application ◽  
Pixel Classification ◽  
Color Texture

scholarly journals Objectness Supervised Merging Algorithm for Color Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Haifeng Sima ◽  
Aizhong Mi ◽  
Zhiheng Wang ◽  
Youfeng Zou
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Region Growing ◽  
Color Image Segmentation ◽  
Semantic Features ◽  
Merging Procedure ◽  
Proposed Model ◽  
Merging Process ◽  
High Level ◽  
Color Texture

Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

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.

Unsupervised color-image segmentation by multicolor space iterative pixel classification

2015 ◽  
Vol 24 (2) ◽  
pp. 023032 ◽  
Author(s):  
Nicolas Vandenbroucke ◽  
Laurent Busin ◽  
Ludovic Macaire
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Pixel Classification

Color Image Segmentation in Both Feature andImage Spaces

2006 ◽  
pp. 209-227 ◽  
Author(s):  
Shengyang Dai ◽  
Yu-Jin Zhang
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Feature Space ◽  
Image Space ◽  
Color Image Segmentation ◽  
Watershed Algorithm ◽  
Random Field Theory ◽  
Irregular Shapes ◽  
Minimization Algorithms ◽  
A New Technique

One critical problem in image segmentation is how to explore the information in both feature and image space and incorporate them together. One approach in this direction is reported in this chapter. Watershed algorithm is traditionally applied on image domain but it fails to capture the global color distribution information. A new technique is to apply first the watershed algorithm in feature space to extract clusters with irregular shapes, and then to use feature space analysis in image space to get the final result by minimizing a global energy function based on Markov random field theory. Two efficient energy minimization algorithms: Graph cuts and highest confidence first (HCF) are explored under this framework. Experiments with real color images show that the proposed two-step segmentation framework is efficient and has been successful in various applications.

scholarly journals Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation

2016 ◽  
Vol 6 (5) ◽  
pp. 1182-1186
Author(s):  
R. V. V. Krishna ◽  
S. Srinivas Kumar
Keyword(s):  
Genetic Algorithms ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Color Image ◽  
Texture Feature ◽  
Color Image Segmentation ◽  
Cluster Center ◽  
Local Descriptor ◽  
Color Feature ◽  
Real Value

This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD) which is a modification of Weber Local Descriptor (WLD) is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.

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