scholarly journals K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 610 ◽  
Author(s):  
Senquan Yang ◽  
Pu Li ◽  
HaoXiang Wen ◽  
Yuan Xie ◽  
Zhaoshui He
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Image Segmentation ◽  
Color Classification ◽  
Clustering Problem ◽  
Illumination Changes ◽  
Segmentation Approach

Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering 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.

Rough Entropy Clustering Algorithm in Image Segmentation

2010 ◽  
pp. 285-304
Author(s):  
Dariusz Malyszko ◽  
Jaroslaw Stepaniuk
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Rough Set ◽  
Rough Sets ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Entropy Measures ◽  
Segmentation Algorithms ◽  
Data Grouping ◽  
Rough Region

Clustering understood as a data grouping technique represents fundamental procedures in image processing. The present chapter’s concerns are combining the concept of rough sets and entropy measures in the area of image segmentation. In this context, comprehensive investigations into rough set entropy based clustering image segmentation techniques have been performed. Segmentation presents low-level image transformation routines concerned with image partitioning into distinct disjoint and homogenous regions. In the area of segmentation routines, threshold based algorithms and clustering algorithms most often are applied in practical solutions when there is a pressing need for simplicity and robustness. Rough entropy threshold based segmentation algorithms simultaneously combine optimal threshold determination with rough region approximations and region entropy measures. In the present chapter, new algorithmic schemes RECA in the area of rough entropy based partitioning routines have been proposed. Rough entropy clustering incorporates the notion of rough entropy into clustering models, taking advantage of dealing with some degree of uncertainty in analyzed data. RECA algorithmic schemes performed usually equally robust compared to standard k-means algorithms. At the same time, in many runs they yielded slightly better performances making possible future implementation in clustering applications.

Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology

Biometrics ◽  
2017 ◽  
pp. 1788-1802 ◽  
Author(s):  
Nihar Ranjan Nayak ◽  
Bikram Keshari Mishra ◽  
Amiya Kumar Rath ◽  
Sagarika Swain
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Clustering Algorithms ◽  
Performance Criterion ◽  
Color Image Segmentation ◽  
Experimental Result ◽  
Image Size ◽  
Impressive Result ◽  
Image Pixels ◽  
Visual Characteristic

The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their proposed Enhanced Clustering method. The idea is to present a brief explanation of the fundamental working principles implicated in these methods. They have analyzed the performance criterion which affects the outcome of segmentation by considering two vital quality measures namely – Structural Content (SC) and Root Mean Square Error (RMSE) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives impressive result for the computed values of SC and RMSE as compared to K-Means and K-Means++. In addition to this, the output of segmentation using the Enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.

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