An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation

Wang, Gaihua; Liu, Yang; Chen, Xiong

doi:10.3390/a8020234

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…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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Section: Introductionmentioning

confidence: 99%

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

Tan¹,

Yang²

2021

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“…To facilitate explanation, the three mentioned clustering strategies for heterogeneous multi-view data, i.e., feature fusion, result fusion, and inter-view collaboration, are generally designated as multi-view clustering in our manuscript. So far, quite a bit of work regarding multi-view clustering has been conducted [7], [12], [20]–[22], [24]–[33], but most of the existing approaches focus on the strategies of feature or result fusion, and the literature associating multi-view learning with MEC is seldom met. As another type of regularization method for crisp k -means [13], [14], MEC is characterized by a more delicate mathematic formulation and a more interpretable connotation than FCM that has been commonly regarded as the most classic representative of soft partition clustering [13], [14], [34]–[36].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Maximum Entropy Clustering by Jointly Leveraging Inter-View Collaborations and Intra-View-Weighted Attributes

et al. 2018

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As a dedicated countermeasure for heterogeneous multi-view data, multi-view clustering is currently a hot topic in machine learning. However, many existing methods either neglect the effective collaborations among views during clustering or do not distinguish the respective importance of attributes in views, instead treating them equivalently. Motivated by such challenges, based on maximum entropy clustering (MEC), two specialized criteria—inter-view collaborative learning (IEVCL) and intra-view-weighted attributes (IAVWA)—are first devised as the bases. Then, by organically incorporating IEVCL and IAVWA into the formulation of classic MEC, a novel, collaborative multi-view clustering model and the matching algorithm referred to as the view-collaborative, attribute-weighted MEC (VC-AW-MEC) are proposed. The significance of our efforts is three-fold: 1) both IEVCL and IAVWA are dedicatedly devised based on MEC so that the proposed VC-AW-MEC is qualified to effectively handle as many multi-view data scenes as possible; 2) IEVCL is competent in seeking the consensus across all involved views throughout clustering, whereas IAVWA is capable of adaptively discriminating the individual impact regarding the attributes within each view; and 3) benefiting from jointly leveraging IEVCL and IAVWA, compared with some existing state-of-the-art approaches, the proposed VC-AW-MEC algorithm generally exhibits preferable clustering effectiveness and stability on heterogeneous multi-view data. Our efforts have been verified in many synthetic or real-world multi-view data scenes.

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“…Traditional state recognition methods mainly combine the field sensor information according to the existing knowledge and experience [7]. However, if the information gathered by sensors is handled without deep fusion, the internal relationship between the information would often be cut off, and the surrounding characteristics indicated by the rational combination of information may be lost [8][9][10]. To realize state recognition accurately, a deep sensor data fusion method should be used, and redundancy and contradiction among the sensors need to be eliminated [11,12].…”

Section: Introductionmentioning

confidence: 99%

A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network

Wang

Tan

et al. 2016

Algorithms

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Due to the traditional state recognition approaches for complex electromechanical equipment having had the disadvantages of excessive reliance on complete expert knowledge and insufficient training sets, real-time state identification system was always difficult to be established. The running efficiency cannot be guaranteed and the fault rate cannot be reduced fundamentally especially in some extreme working conditions. To solve these problems, an online state recognition method for complex equipment based on a fuzzy probabilistic neural network (FPNN) was proposed in this paper. The fuzzy rule base for complex equipment was established and a multi-level state space model was constructed. Moreover, a probabilistic neural network (PNN) was applied in state recognition, and the fuzzy functions and quantification matrix were presented. The flowchart of proposed approach was designed. Finally, a simulation example of shearer state recognition and the industrial application with an accuracy of 90.91% were provided and the proposed approach was feasible and efficient.

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An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation

Cited by 7 publications

References 17 publications

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

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

Multi-View Maximum Entropy Clustering by Jointly Leveraging Inter-View Collaborations and Intra-View-Weighted Attributes

A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network

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