Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering

Salmi, Abderezak; Hammouche, Kamal; Macaire, Ludovic

doi:10.1117/1.jei.30.1.013014

Cited by 5 publications

(9 citation statements)

References 42 publications

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“…For each pixel, by counting the number of pixels with each representative color within a circular neighborhood, a local histogram was obtained. Salmi et al [19] proposed a new semi-supervised method by combining constrained feature selection and spectral clustering (SC) to perform color-texture image segmentation. Baya et al [20] adopted a clustering validation method, Clustering Stability (CS), to automatically segment images.…”

Section: Related Workmentioning

confidence: 99%

An Image Segmentation Method Based on the Law of Universal Gravitation and Adaptive Affinity Propagation Spectrum Clustering

Liu¹,

Zhu

2022

Mathematical Problems in Engineering

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There are many problems of division in natural and social sciences, and with the development of science and technology, the requirement for division is also increasing. It is difficult to divide accurately by experience and expertise alone, and the most important research branch of the division problem is the clustering algorithm. It is to group similar samples into one class and divide the elements with large differences into different classes. Due to the simplicity and efficiency of the clustering algorithm, it is widely used in image segmentation. The conventional Spectral Clustering (SC) algorithm cannot recognize nonconvex data and has the disadvantage of strong dependence on biased parameter values. To address this problem, the Gravity-based Adaptive Spectral Clustering (GASC) algorithm is proposed in this study. Based on the conventional SC algorithm, the algorithm uses gravity to calculate the similarity (gravity) between data, and uses information entropy and adaptive enhancement (AdaBoost) algorithms to obtain the weights of correct cluster sampling points and wrong cluster sampling points in each cluster, so as to reduce the dependence of the algorithm on bias parameters and reduce the number of wrongly divided sample points. Meanwhile, the GASC algorithm is applied to image segmentation. The implementation process includes three stages: image preprocessing, feature extraction, and clustering. The comparison experiments show that the mean values of normalization and accuracy of the GASC algorithm are improved compared with other clustering algorithms, and the segmentation accuracy for images is higher.

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Section: Related Workmentioning

confidence: 99%

An Image Segmentation Method Based on the Law of Universal Gravitation and Adaptive Affinity Propagation Spectrum Clustering

Liu¹,

Zhu

2022

Mathematical Problems in Engineering

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“…Formula (1) shows that the reflected light is only affected by CH and ZD. However, the impact of CH is far greater than that of ZD.…”

Section: Figure 1 Utilization Frequencies Of Different Color Systems In Landscape Imagesmentioning

confidence: 99%

“…Owing to seasonal changes and the variation in spatial layout, landscapes present various textures and color types. The varied colors would trigger different emotions among the viewers, exert a positive influence on their psychology, and leave them with a rich impression of the landscapes [1][2][3][4][5][6]. The dominant color features determine the presentation effect and visual experience of landscapes [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Color Features from Landscape Images Based on Image Processing

Tan¹,

Yang²

2021

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The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

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“…However, one of the most important features used to distinguish various regions of an image is texture. Versatility of textures applications has been shown clearly: Pattern recognition, image classification, image segmentation (Salmi et al, 2021), image enhancement, image compression, fault detection (Sun et al, 2009), medical imaging diagnosis (Duncan and Ayache, 2000), and analysis of material structure are examples of generic (Da Costa et al, 2015) activities that can be focused on the concept of texture. Texture synthesis aims to infer a generating process from an example texture, allowing the production of an infinite number of new samples of that texture.…”

Section: Introductionmentioning

confidence: 99%

Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models

Maarouf¹,

Hachouf

Kharfouchi

2023

Image Anal Stereol

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Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches.

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Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering

Cited by 5 publications

References 42 publications

An Image Segmentation Method Based on the Law of Universal Gravitation and Adaptive Affinity Propagation Spectrum Clustering

An Image Segmentation Method Based on the Law of Universal Gravitation and Adaptive Affinity Propagation Spectrum Clustering

Automatic Extraction of Color Features from Landscape Images Based on Image Processing

Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models

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