Methods of Classification of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Components

Daradkeh, Yousef Ibrahim; Gorokhovatskyi, Volodymyr; Tvoroshenko, Iryna; Gadetska, Svitlana; Al‐Dhaifallah, Mujahed

doi:10.1109/access.2021.3093457

Cited by 6 publications

(45 citation statements)

References 29 publications

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“…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%

“…Modern structural methods for analyzing visual information are based on evaluating the similarity of descriptor descriptions of the analyzed and etalon images, represented as the set of descriptors of keypoints [10,21,[24][25][26]. The value of similarity reflects the degree of response of the binary relation of relevance for two information units as descriptions of visual objects.…”

Section: Related Workmentioning

confidence: 99%

“…The task of the splitting for obtaining the model of etalons as groups of structural elements is solved in different ways [21,27].…”

Section: Related Workmentioning

confidence: 99%

“…During implementation methods of structural classification, descriptions of objects are represented as high-dimensional numerical vectors [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…The number of descriptors reaches 1500 elements. The transformation of the feature space in the form of a cluster system significantly simplifies their implementation [18,21]. The main tool is the cluster data analysis, based on the metric relations on the set of descriptions and the definition of similar classes of the objects and etalons [18,21,27].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Problem Statementsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The task of the splitting for obtaining the model of etalons as groups of structural elements is solved in different ways [21,27].…”

Section: Related Workmentioning

confidence: 99%

“…During implementation methods of structural classification, descriptions of objects are represented as high-dimensional numerical vectors [21,22].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Tools for Fast Metric Data Search in Structural Methods for Image Classification

et al. 2022

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The article proposes a new classification method based on implementing the high-speed search tools for the indexed data structure created on the etalon set of features, which has significant advantages in processing speed compared to the traditional approaches. The classifier is represented as twostage processing, where at the first stage the class for the separate object descriptor is determined, and at the second stage, the resulting class of the object is determined based on the obtained set of local solutions. The developed method is based on the preliminary construction of the indexed hash structures for the set of descriptors of the base of the etalon images. Implementing the hash representation allows for increasing the speed of identification or classification of visual objects. A comparative experiment with the traditional method of voting has been conducted, where the linear search for the nearest descriptor has been implemented for the identification without the use of prior creation of the indexed hash representation of the etalons. In the experiment, we have gained in processing speed for the developed method compared to the traditional over 10 times. The gain in processing speed increases proportionally with the number of the etalons and the number of the descriptors in the descriptions. The experiment has shown that the efficiency of the method can be enhanced by varying the values of its parameters and adapting to the properties of the data.

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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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Methods of Classification of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Components

Cited by 6 publications

References 29 publications

Cluster Representation of the Structural Description of Images for Effective Classification

Cluster Representation of the Structural Description of Images for Effective Classification

Tools for Fast Metric Data Search in Structural Methods for Image Classification

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

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