Image classification based on the kohonen network and the data space modification

Gorokhovatskyi, Volodymyr; Tvoroshenko, Iryna

doi:10.32782/cmis/2608-76

Cited by 6 publications

(22 citation statements)

References 17 publications

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“…The proposed classification methods based on the cluster representation have undergone detailed experimental verification on various image bases [10,20,27].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This fact is due to the creation and application of main characteristics, namely the set of description centers. In this case, the segmenting of the etalon data is performed at the preliminary step and does not affect the performance of the grouping [18,20].…”

Section: Problem Statementsmentioning

confidence: 99%

“…In the general case, for the most common clustering procedures which use vector data with non-integer components (k-mean, hierarchical classification, etc. [20,21,35]), Euclidean or Manhattan distances can be used.…”

Section: Problem Statementsmentioning

confidence: 99%

“…The modern deep learning methods using specialized processing networks aim to identify meaningful differences in the classified data to achieve the most significant performance indicators for fixed datasets [2][3][4][5]29,32]. At the same time, methods under development, which due to the combined implementation of the principles of local and global data analysis make it possible to significantly simplify the classification process while ensuring the required level of efficiency [12,[18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

“…Achieving high-efficiency indicators for classification methods requires solving problems associated with the multidimensional data [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Cluster Representation of the Structural Description of Images for Effective Classification

Daradkeh¹,

Gorokhovatskyi²,

Tvoroshenko³

et al. 2022

Computers, Materials &Amp; Continua

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The problem of image recognition in the computer vision systems is being studied. The results of the development of efficient classification methods, given the figure of processing speed, based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided. We propose three versions of the classifier according to the following principles: "object-etalon", "object descriptor-etalon" and "vector description of the object-etalon", which are not similar in level of integration of researched data analysis. The options for constructing clusters over the whole set of descriptions of the etalon database, separately for each of the etalons, as well as the optimal method to compare sets of segment centers for the etalons and object, are implemented. An experimental rating of the efficiency of the created classifiers in terms of productivity, processing time, and classification quality has been realized of the applied. The proposed methods classify the set of etalons without error. We have formed the inference about the efficiency of classification approaches based on segment centers. The time of image processing according to the developed methods is hundreds of times less than according to the traditional one, without reducing the accuracy.

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“…The proposed classification methods based on the cluster representation have undergone detailed experimental verification on various image bases [10,20,27].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Problem Statementsmentioning

confidence: 99%

Section: Problem Statementsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Achieving high-efficiency indicators for classification methods requires solving problems associated with the multidimensional data [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cluster Representation of the Structural Description of Images for Effective Classification

Daradkeh¹,

Gorokhovatskyi²,

Tvoroshenko³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

et al. 2021

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The processes of intelligent data processing in computer vision systems have been researched. The problem of structural image recognition is relevant. This is a promising way to assess the degree of similarity of objects. This approach provides the simplicity of construction and the high reliability of decision making. The main problem of an effective description of characteristic features is the distortion of fragments of analyzed objects. The reasons for changing the input data can be the actions of geometric transformations, the influence of background or interference. The elements of false objects with similar characteristics are formed. The problem of ensuring high-quality recognition requires the implementation of effective means of image processing. Methods of statistical modeling, granulation of data and fuzzy sets, detection and comparison of keypoints on the image, classification and clustering of data, and simulation modelling are used in this research. The implementation of the proposed approaches provides the formation of a concise description of features or a vector representation of unique keypoints. The verification of theoretical foundations and evaluation of the effectiveness of the proposed data processing methods for real image bases is performed using the OpenCV library. The applied significance of the work is substantiated according to the criterion of data processing time without reducing the characteristics of reliability and interference immunity. The developed methods allow to increase the structural recognition of images by several times. Perspectives of research may involve identifying the optimal number of keypoints of the base set.

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Statistical Data Analysis Models for Determining the Relevance of Structural Image Descriptions

Daradkeh,

Gorokhovatskyi,

Tvoroshenko

et al. 2023

IEEE Access

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The aim of the research is to improve the effectiveness of image recognition methods according to the description in the form of a set of keypoint descriptors. The focus is on increasing the speed of analysis and processing of description data while maintaining the required level of classification efficiency. The class of the image is provided as a description of the etalon. It is proposed to transform the description by implementing a statistical system of features for non-intersecting data fragments. The developed method is based on the aggregation of data distribution values within the description, the basis of which is the bit representation of the descriptors. Statistical features are calculated as the frequency of occurrence of the fixed value of a fragment on a set of description data and thus reflect the individual properties of images. Three main classifier models are analyzed: calculating the measure of data relevance in the form of distributions; assigning each of the descriptors to defined classes (voting); using the apparatus of statistical data analysis to decide on the significance of the difference between the distributions of the object and etalons. The results of software modeling of methods and calculations of statistical significance of differences based on distributions for training sets of images are represented. Using distributions instead of a set of descriptors increases the processing speed by hundreds of times, while the classification accuracy is maintained at a sufficient level and does not deteriorate compared to traditional voting.INDEX TERMS Computer vision, data fragment, descriptor, image classification, keypoint.

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Image classification based on the kohonen network and the data space modification

Cited by 6 publications

References 17 publications

Cluster Representation of the Structural Description of Images for Effective Classification

Cluster Representation of the Structural Description of Images for Effective Classification

Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

Statistical Data Analysis Models for Determining the Relevance of Structural Image Descriptions

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