Image Texture, Texture Features, and Image Texture Classification and Segmentation

Hung, Chih‐Cheng; Song, Enmin; Lan, Yihua

doi:10.1007/978-3-030-13773-1_1

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…In [8], the method of segmentation using fuzzy clustering of C-averages is considered, with the help of which clusters and their labels are automatically selected. Fuzzy entropy is used to determine the number of clusters.…”

Section: Highlighting Brightness Channels In the Red-green-blue Color...mentioning

confidence: 99%

“…Image pixels are classified by the corresponding clusters based on the minimum Euclidean distance. The advantages of [8] are the lack of a priori information for the segmentation of the color area; in addition, there is no obvious distortion or color change after segmentation. The disadvantage of [8] is the failure to take into consideration local information in the context of the image, which makes it sensitive to additive noise and impairs the characteristics of the pixels of the image.…”

Section: Highlighting Brightness Channels In the Red-green-blue Color...mentioning

confidence: 99%

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Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Khudov

Makoveichuk

Butko

et al. 2022

EEJET

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The object of this research is the process of segmentation of camouflaged military equipment in images from space surveillance systems. The method of segmentation of camouflaged military equipment in images from space surveillance systems has been improved using a genetic algorithm. Unlike known methods, the method of segmentation of camouflaged military equipment using a genetic algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – the use of a genetic algorithm in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to the formation of generations and populations of chromosomes, the calculation of the objective function, selection, crossing, mutation, and decoding of chromosomes in each brightness channel of the Red-Green-Blue color space. Experimental studies were conducted on the segmentation of camouflaged military equipment using a genetic algorithm. It is established that the improved method of segmentation using a genetic algorithm makes it possible to segment images from space surveillance systems. A comparison of the quality of segmentation was carried out. It is established that the improved method of segmentation using a genetic algorithm reduces segmentation errors in the following way: – compared to the known k-means method, by an average of 15 % of errors of the first kind and an average of 7 % of errors of the second kind; – compared to the method of segmentation based on the algorithm of swarm of particles, by an average of 3.8 % of errors of the first kind and an average of 2.9 % of errors of the second kind. The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems

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Section: Highlighting Brightness Channels In the Red-green-blue Color...mentioning

confidence: 99%

Section: Highlighting Brightness Channels In the Red-green-blue Color...mentioning

confidence: 99%

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Khudov

Makoveichuk

Butko

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

“…In [27], a method of segmentation based on the clustering of C-means is proposed. To classify pixels, the minimum Euclidean distance and fuzzy entropy are used.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The method provides a reduction in segmentation errors (of the first and second kinds) on average from 3 % to 15 %. In [27,28], segmentation methods based on clustering of k-means and C-means are proposed.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Methods of UAVs images segmentation based on k-means and a genetic algorithm

Рубан

Khudov

Makoveichuk

et al. 2022

EEJET

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The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined. An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones, image segmentation by a k-means-based method that successfully works on images from space surveillance systems cannot be directly applied to image segmentation from unmanned aerial vehicles. Unlike known ones, image segmentation by a method based on a genetic algorithm that successfully works on images from space surveillance systems also cannot be directly applied to image segmentation from unmanned aerial vehicles. The quality of segmentation of images from unmanned aerial vehicles by methods based on k-means and a genetic algorithm was assessed. It was established that: – the average level of first-kind errors is 70 % and 51 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively; – average level of second-kind errors is 61 % and 43 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively. It was concluded that further research must be carried out to develop methods for segmenting images from unmanned aerial vehicles.

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“…It expresses how efficient descriptors are required. The image texture denotes the arrangement, appearance and structure of fragments of an object inside an image [2]. If we consider the context of clinical field, the images utilized for diagnostic motives are mostly 2 dimensional and digital.…”

Section: Introductionmentioning

confidence: 99%

Development of a local binary pattern descriptor for texture analysis using deep convolutional neural network

SINGH,

GAGAN

2024

Preprint

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The machine learning is an important branch of artificial intelligence. In machine learning, the facial expression recognition continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary patterns and its extended variants. This research paper expresses the strength of deep learning techniques that is the deep convolutional neural networks for classification of faces in selected dataset. We applied proposed Deep CNN architecture with local binary patterns and histogram of oriented gradient method. Viola Jones algorithm is applied for detection of faces from Face Recognition dataset. The features of faces are extracted by Binary Phase Component Analysis and information correlation factor gain. Then the classification of images is performed by our proposed deep convolutional neural network (Deep CNN). The performance of the model is evaluated by accuracy, precision, recall, f1-score and confusion matrix. The architecture of CNN constitutes convolutional layer, max-pooling, dense and flatten layers with dropout. The proposed architecture is validated on the Face Recognition dataset. We obtained 0.98 as accuracy which is the very high accuracy of deep CNN model for classification.

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Image Texture, Texture Features, and Image Texture Classification and Segmentation

Cited by 10 publications

References 30 publications

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Methods of UAVs images segmentation based on k-means and a genetic algorithm

Development of a local binary pattern descriptor for texture analysis using deep convolutional neural network

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