-
1دورية أكاديميةApplication of multi-component data model for class descriptions in the image classification problem
المؤلفون: Volodymyr Gorokhovatskyi, Natalia Stiahlyk, Oleksii Zhadan
المصدر: Сучасні інформаційні системи, Vol 6, Iss 1, Pp 5-11 (2022)
مصطلحات موضوعية: computer vision, structural methods of image classification, orb descriptor, data center system, multicomponent model, effectiveness of classification, Computer software, QA76.75-76.765, Information theory, Q350-390
الوصف: The subject of research of the article is the methods of image classification according to the set of descriptors of key points in computer vision systems. The aim is to increase the efficiency of classification by introducing a multicomponent data model on a set of descriptors for the base of reference images. Applied methods: ORB detector and descriptors, apparatus of set theory and vector space, metric models for determining the relevance of sets of multidimensional vectors, elements of probability theory, software modeling. Results are obtained: a modified method of image classification based on the introduction of a multicomponent model for data analysis with a system of centers is developed, methods of constructing a set of data centers are identified, the most effective is the set medoid and centers based on it. The effectiveness of the modification significantly depends on the method of forming the centers, the applied classification model, as well as on the data itself. The best results were shown by the classification with the integrated indicator separately for each of the standards in the form of the sum of the values of the distributions for the set of centers; experimentally tested the effectiveness of the classification, confirmed the efficiency of the proposed method. The practical significance of the work is the construction of classification models in the transformed data space, confirmation of the efficiency of the proposed modifications on the examples of images, the creation of software for the implementation of developed classification methods in computer vision systems.
وصف الملف: electronic resource
-
2دورية أكاديمية
المصدر: Сучасні інформаційні системи; Том 7 № 1 (2023): Сучасні інформаційні системи; 5-13 ; Advanced Information Systems; Vol. 7 No. 1 (2023): Advanced Information Systems; 5-13 ; Современные информационные системы - Sučasnì ìnformacìjnì sistemi; Том 7 № 1 (2023): Advanced Information Systems; 5-13 ; 2522-9052
مصطلحات موضوعية: комп’ютерний зір, структурні методи класифікації, дескриптор AKAZE, матриця відстаней, розподіл даних, результативність класифікації, computer vision, structural image classification methods, AKAZE descriptor, distance matrix, data distribution, effectiveness of classification
الوصف: The subject of the paper is the methods of image classification in computer vision systems. The goal is the further development of structural classification methods in terms of introducing a system of classification features based on the values of the distance matrix for multidimensional description components. Applied methods: AKAZE keypoint detector, set theory and vector spaces methods, metric models for determining relevance for a set of multidimensional vectors, theory of data distribution formation, elements of probability theory, software modeling. Results: modifications of the image classification method based on the implementation of the formalism of distance matrices for a set of description components have been developed, integration models for the formation of classification features and actions on sets of vectors based on the distance matrix have been proposed, metric features of a set of multidimensional vectors as classification features have been established. The effectiveness of the developed modifications of the classifier depends on the choice of a subset and the number of descriptors in the description, a measure for comparing descriptions. Based on the introduction of the distance matrix, it was possible to form built-in features in the form of one-dimensional data distributions and reduce computational costs while ensuring the effectiveness of classification on the training data set. The practical significance of the work is the formation of classification models based on the distance matrix, confirming the performance of the proposed modifications using image examples, and creating a software application that applies the proposed classifiers in computer vision. ; Предметом досліджень статті є методи класифікації зображень у системах комп’ютерного зору. Мета – розвинення структурних методів класифікації в аспекті впровадження системи класифікаційних ознак на підґрунті значень матриці відстаней для багатовимірних компонентів опису. Застосовувані методи: детектор ключових точок AKAZE, апарат ...
وصف الملف: application/pdf
العلاقة: http://ais.khpi.edu.ua/article/view/274850/270000Test; http://ais.khpi.edu.ua/article/view/274850Test
الإتاحة: https://doi.org/10.20998/2522-9052.2023.1.01Test
http://ais.khpi.edu.ua/article/view/274850Test -
3دورية أكاديمية
المصدر: Сучасні інформаційні системи, Vol 5, Iss 1, Pp 5-11 (2021)
مصطلحات موضوعية: computer vision, image classification, key point, orb detector, descriptor, description center, statistical distribution, description relevance, classification effectiveness, Computer software, QA76.75-76.765, Information theory, Q350-390
الوصف: The subject of research is models for constructing image classifiers in the description space as a set of descriptors of key points in the recognition of visual objects in computer vision systems. The goal is to create and study the properties of the image classifier based on the construction of an ensemble of distributions for the components of the structural description using various models of classification decisions, which provides effective classification. Tasks: construction of classification models in the synthesized space of images of probability distributions, analysis of parameters influencing their efficiency, experimental evaluation of the effectiveness of classifiers by means of software modeling based on the results of processing the experimental image base. The applied methods are: ORB detector for formation of keypoint descriptors, data mining, mathematical statistics, means of determining relevance for sets of data vectors, software modeling. The obtained results: The developed method of classification confirms its efficiency and effectiveness for image classification. The effectiveness of the method can be enhanced by the introduction of a variety of types of metrics and measures of similarity between centers and descriptors, by the choice of method of forming centers for reference etalon descriptions, by the introduction of logical processing and compression of the structural description. The best results of the classification were shown by the model using the most important class by the distribution vector for each descriptor corresponding to the mode parameter. The use of a concentrated part of the description data makes it possible to improve its distinction from other descriptions. The use of the median as the center of description has an advantage over the mean. Conclusions. Scientific novelty is the development of an effective method of image classification based on the introduction of a system of probability distributions for data components, which contributes to indepth analysis in the data space and increases in classification effectiveness. The classifier is implemented in the variants of comparing the integrated representation of distributions by classes and on the basis of mode analysis for the distributions of individual components. The practical importance of the work is the construction of classification models in the modified data space, confirmation of the efficiency of the proposed modifications of data analysis on examples of images, development of software models for implementation of the proposed classification methods in computer vision systems.
وصف الملف: electronic resource
-
4دورية أكاديمية
المصدر: Сучасні інформаційні системи; Том 6 № 3 (2022): Сучасні інформаційні системи; 5-12 ; Advanced Information Systems; Vol. 6 No. 3 (2022): Advanced Information Systems; 5-12 ; Современные информационные системы - Sučasnì ìnformacìjnì sistemi; Том 6 № 3 (2022): Advanced Information Systems; 5-12 ; 2522-9052
مصطلحات موضوعية: комп’ютерний зір, структурні методи класифікації зображень, дескриптор ORB, ортогональне розкладення, функції Уолша, швидкодія оброблення, результативність класифікації, computer vision, structural methods of image classification, ORB descriptor, orthogonal decomposition, Walsh functions, processing speed, effectiveness of classification
الوصف: The subject of the article's research is the improvement of structural methods of image classification in computer vision systems. The goal is to reduce computational costs for classification by implementing a device for decomposing image description components using a system of orthogonal functions and implementing feature space compression models. Applied methods: ORB key point detector, set theory apparatus and vector spaces, metric models for determining relevance to sets of multidimensional vectors, theory of orthogonal decomposition of vectors, elements of probability theory, software modeling. Obtained results: modifications of the image classification method based on the introduction of orthogonal data decomposition in vector space were developed, models were proposed for data compression in the transformed feature space, Tanimoto metric was introduced for image comparison, a threshold selection method was established for determining equivalent description components. The effectiveness of the developed modifications of the classifier depends on the selection of a subset of functions for decomposition, the metric for comparing descriptions, and the method of determining the equivalence threshold. The implementation of the apparatus of orthogonal functions not only reduced computational costs tenfold, but also ensured sufficiently high indicators of classification performance and interference resistance. The practical significance of the work is the construction of new models of the image classifier in the transformed space of features, confirmation of the functionality, speed and immunity of the proposed modifications on examples of images, the creation of a software application for the implementation of the developed classification methods in computer vision systems. ; Предметом досліджень статті є удосконалення структурних методів класифікації зображень у системах комп’ютерного зору. Метою є скорочення обчислювальних витрат на класифікацію шляхом впровадження апарату розкладання компонентів опису ...
وصف الملف: application/pdf
العلاقة: http://ais.khpi.edu.ua/article/view/263963/260161Test; http://ais.khpi.edu.ua/article/view/263963Test
الإتاحة: https://doi.org/10.20998/2522-9052.2022.3.01Test
http://ais.khpi.edu.ua/article/view/263963Test -
5دورية أكاديمية
المصدر: Сучасні інформаційні системи; Том 6 № 1 (2022): Сучасні інформаційні системи; 5-11 ; Advanced Information Systems; Vol. 6 No. 1 (2022): Advanced Information Systems; 5-11 ; Современные информационные системы - Sučasnì ìnformacìjnì sistemi; Том 6 № 1 (2022): Advanced Information Systems; 5-11 ; 2522-9052
مصطلحات موضوعية: комп’ютерний зір, структурні методи класифікації зображень, дескриптор ORB, система центрів даних, багатокомпонентна модель, медоїд множини, результативність класифікації, computer vision, structural methods of image classification, ORB descriptor, data center system, multicomponent model, medoid set, effectiveness of classification
الوصف: The subject of research of the article is the methods of image classification according to the set of descriptors of key points in computer vision systems. The aim is to increase the efficiency of classification by introducing a multicomponent data model on a set of descriptors for the base of reference images. Applied methods: ORB detector and descriptors, apparatus of set theory and vector space, metric models for determining the relevance of sets of multidimensional vectors, elements of probability theory, software modeling. Results are obtained: a modified method of image classification based on the introduction of a multicomponent model for data analysis with a system of centers is developed, methods of constructing a set of data centers are identified, the most effective is the set medoid and centers based on it. The effectiveness of the modification significantly depends on the method of forming the centers, the applied classification model, as well as on the data itself. The best results were shown by the classification with the integrated indicator separately for each of the standards in the form of the sum of the values of the distributions for the set of centers; experimentally tested the effectiveness of the classification, confirmed the efficiency of the proposed method. The practical significance of the work is the construction of classification models in the transformed data space, confirmation of the efficiency of the proposed modifications on the examples of images, the creation of software for the implementation of developed classification methods in computer vision systems. ; Предметом досліджень статті є методи класифікації зображень за множиною дескрипторів ключових точок у системах комп’ютерного зору. Метою є підвищення ефективності класифікації шляхом впровадження багатокомпонентної моделі даних на множині дескрипторів для бази еталонних образів. Застосовувані методи: детектор та дескриптори ORB, апарат теорії множин і векторного простору, метричні моделі визначення релевантності для ...
وصف الملف: application/pdf
العلاقة: http://ais.khpi.edu.ua/article/view/254190/251474Test; http://ais.khpi.edu.ua/article/view/254190Test
الإتاحة: https://doi.org/10.20998/2522-9052.2022.1.01Test
http://ais.khpi.edu.ua/article/view/254190Test -
6دورية أكاديمية
المؤلفون: Pavlo Pustovoitov, Maxim Okhrimenko, Vitalii Voronets, Dmitry Udalov
المصدر: Сучасні інформаційні системи, Vol 5, Iss 3 (2021)
مصطلحات موضوعية: computer vision, structural methods of image classification, ORB descriptor, hashing, linear search, hash, Computer software, QA76.75-76.765, Information theory, Q350-390
الوصف: The subject of this research is the image classification methods based on a set of key points descriptors. The goal is to increase the performance of classification methods, in particular, to improve the time characteristics of classification by introducing hashing tools for reference data representation. Methods used: ORB detector and descriptors, data hashing tools, search methods in data arrays, metrics-based apparatus for determining the relevance of vectors, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using hash structures, which speeds up the calculation dozens of times; the classification time for the considered experimental descriptions increases linearly with decreasing number of hashes; the minimum metric value limit choice on setting the class for object descriptors significantly affects the accuracy of classification; the choice of such limit can be optimized for fixed samples databases; the experimentally achieved accuracy of classification indicates the efficiency of the proposed method based on data hashing. The practical significance of the work is - the classification model’s synthesis in the hash data representations space, efficiency proof of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems.
وصف الملف: electronic resource
-
7دورية أكاديمية
المؤلفون: Gorokhovatskyi, Volodymyr, Vlasenko, Nataliia
المصدر: Сучасні інформаційні системи; Том 5 № 4 (2021): Сучасні інформаційні системи; 10-16 ; Advanced Information Systems; Vol. 5 No. 4 (2021): Advanced Information Systems; 10-16 ; Современные информационные системы - Sučasnì ìnformacìjnì sistemi; Том 5 № 4 (2021): Advanced Information Systems; 10-16 ; 2522-9052
مصطلحات موضوعية: computer vision, structural methods of image classification, BRISK descriptor, hashing based on proximity, hash, classification performance, комп’ютерний зір, структурні методи класифікації зображень, редукція опису, інформативність даних, метричний критерій, швидкодія класифікації
الوصف: The subject of the research is the methods of image classification on a set of key point descriptors in computer vision systems. The goal is to improve the performance of structural classification methods by introducing indexed hash structures on the set of the dataset reference images descriptors and a consistent chain combination of several stages of data analysis in the classification process. Applied methods: BRISK detector and descriptors, data hashing tools, search methods in large data arrays, metric models for the vector relevance estimation, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using indexed hash structures, that speeds up the calculation dozens of times; the gain in computing time increases with an increase of the number of reference images and descriptors in descriptions; the peculiarity of the classifier is that not an exact search is performed, but taking into account the permissible deviation of data from the reference; experimentally verified the effectiveness of the classification, which indicates the efficiency and effectiveness of the proposed method. The practical significance of the work is the construction of classification models in the transformed space of the hash data representation, the efficiency confirmation of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems. ; Предметом досліджень статті є класифікатори зображень за множиною дескрипторів ключових точок. Метою є підвищення продуктивності методів класифікації, зокрема, скорочення обчислювальних затрат шляхом впровадження на попередньому етапі оброблення апарату редукції для подання еталонних даних. Методи, що застосовуються: метричний апарат у векторному просторі, моделі для оцінювання інформативності даних, методи пошуку в масивах даних, моделі для визначення релевантності векторів та множин векторів, ...
وصف الملف: application/pdf
العلاقة: http://ais.khpi.edu.ua/article/view/247261/244575Test; http://ais.khpi.edu.ua/article/view/247261Test
الإتاحة: https://doi.org/10.20998/2522-9052.2021.4.02Test
http://ais.khpi.edu.ua/article/view/247261Test -
8دورية أكاديمية
المصدر: Сучасні інформаційні системи; Том 5 № 3 (2021): Сучасні інформаційні системи; 5-12 ; Advanced Information Systems; Vol. 5 No. 3 (2021): Advanced Information Systems; 5-12 ; Современные информационные системы - Sučasnì ìnformacìjnì sistemi; Том 5 № 3 (2021): Advanced Information Systems; 5-12 ; 2522-9052
مصطلحات موضوعية: комп’ютерний зір, структурні методи класифікації зображень, дескриптор BRISK, хешування із врахуванням близькості, хеш-кошик, швидкодія класифікації, computer vision, structural methods of image classification, BRISK descriptor, hashing based on proximity, hash, classification performance
الوصف: The subject of research of the paper is the methods of image classification on a set of key point descriptors in computer vision systems. The goal is to improve the performance of structural classification methods by introducing indexed hash structures on the set of the dataset reference images descriptors and a consistent chain combination of several stages of data analysis in the classification process. Applied methods: BRISK detector and descriptors, data hashing tools, search methods in large data arrays, metric models for the vector relevance estimation, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using indexed hash structures, that speeds up the calculation dozens of times; the gain in computing time increases with an increase of the number of reference images and descriptors in descriptions; the peculiarity of the classifier is that not an exact search is performed, but taking into account the permissible deviation of data from the reference; experimentally verified the effectiveness of the classification, which indicates the efficiency and effectiveness of the proposed method. The practical significance of the work is the construction of classification models in the transformed space of the hash data representation, the efficiency confirmation of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems. ; Предметом досліджень статті є методи класифікації зображень за множиною дескрипторів ключових точок у системах комп’ютерного зору. Метою є підвищення продуктивності структурних методів класифікації шляхом впровадження індексованих хеш-структур на множині дескрипторів бази еталонних образів та узгодженого ланцюжкового поєднання кількох етапів аналізу даних у процесі класифікації. Застосовувані методи: детектор та дескриптори BRISK, засоби хешування даних, методи пошуку в об’ємних масивах даних, ...
وصف الملف: application/pdf
العلاقة: http://ais.khpi.edu.ua/article/view/242183/240135Test; http://ais.khpi.edu.ua/article/view/242183Test
الإتاحة: https://doi.org/10.20998/2522-9052.2021.3.01Test
http://ais.khpi.edu.ua/article/view/242183Test -
9دورية أكاديمية
المصدر: Сучасні інформаційні системи; Том 5 № 2 (2021): Сучасні інформаційні системи; 13-20 ; Advanced Information Systems; Vol. 5 No. 2 (2021): Advanced Information Systems; 13-20 ; Современные информационные системы - Sučasnì ìnformacìjnì sistemi; Том 5 № 2 (2021): Advanced Information Systems; 13-20 ; 2522-9052
مصطلحات موضوعية: computer vision, structural methods of image classification, ORB descriptor, hashing, linear search, hash, processing speed, classification accuracy, комп’ютерний зір, структурні методи класифікації зображень, дескриптор ORB, хешування, лінійний пошук, хеш-кошик, швидкодія оброблення, точність класифікації
الوصف: The subjectof this research is the image classification methods based on a set of key points descriptors. The goal is to increase the performance of classification methods, in particular, to improve the time characteristics of classification by introducing hashing tools for reference data representation. Methods used: ORB detector and descriptors, data hashing tools, search methods in data arrays, metrics-based apparatus for determining the relevance of vectors, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using hash structures, which speeds up the calculation dozens of times; the classification time for the considered experimental descriptions increases linearly with decreasing number of hashes; the minimum metric value limit choice on setting the class for object descriptors significantly affects the accuracy of classification; the choice of such limit can be optimized for fixed samples databases; the experimentally achieved accuracy of classification indicates the efficiency of the proposed method based on data hashing. The practical significanceof the work is - the classification model’s synthesis in the hash data representations space, efficiency proof of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems. ; Предметом досліджень є методи класифікації зображень за множиною дескрипторів ключових точок. Метою є підвищення продуктивності методів класифікації, зокрема, прискорення часових показників класифікації шляхом впровадження засобів хешування для подання еталонних даних. Методи, що застосовуються: детектор та дескриптори ORB, засоби хешування даних, методи пошуку в масивах даних, апарат визначення релевантності векторів на основі метрик, програмне моделювання. Отримані результати: розроблено ефективний метод класифікації зображень на основі впровадження швидкісного пошуку із використанням ...
وصف الملف: application/pdf
العلاقة: http://ais.khpi.edu.ua/article/view/234962/233460Test; http://ais.khpi.edu.ua/article/view/234962Test
الإتاحة: https://doi.org/10.20998/2522-9052.2021.2.02Test
http://ais.khpi.edu.ua/article/view/234962Test -
10دورية أكاديمية
المصدر: Сучасні інформаційні системи; Том 5 № 1 (2021): Сучасні інформаційні системи; 5-11 ; Advanced Information Systems; Vol. 5 No. 1 (2021): Advanced Information Systems; 5-11 ; Современные информационные системы - Sučasnì ìnformacìjnì sistemi; Том 5 № 1 (2021): Современные информационные системы; 5-11 ; 2522-9052
مصطلحات موضوعية: комп’ютерний зір, класифікація зображень, ключова точка, детектор ORB, дескриптор, центр опису, статистичний розподіл, релевантність описів, результативність класифікації, computer vision, image classification, key point, ORB detector, descriptor, description center, statistical distribution, description relevance, classification effectiveness, компьютерное зрение, классификация изображений, ключевая точка, центр описания, статистическое распределение, релевантность описаний, результативность классификации
الوصف: The subject of research is models for constructing image classifiers in the description space as a set of descriptors of key points in the recognition of visual objects in computer vision systems. The goal is to create and study the properties of the image classifier based on the construction of an ensemble of distributions for the components of the structural description using various models of classification decisions, which provides effective classification. Tasks: construction of classification models in the synthesized space of images of probability distributions, analysis of parameters influencing their efficiency, experimental evaluation of the effectiveness of classifiers by means of software modeling based on the results of processing the experimental image base. The applied methods are: ORB detector for formation of keypoint descriptors, data mining, mathematical statistics, means of determining relevance for sets of data vectors, software modeling. The obtained results: The developed method of classification confirms its efficiency and effectiveness for image classification. The effectiveness of the method can be enhanced by the introduction of a variety of types of metrics and measures of similarity between centers and descriptors, by the choice of method of forming centers for reference etalon descriptions, by the introduction of logical processing and compression of the structural description. The best results of the classification were shown by the model using the most important class by the distribution vector for each descriptor corresponding to the mode parameter. The use of a concentrated part of the description data makes it possible to improve its distinction from other descriptions. The use of the median as the center of description has an advantage over the mean. Conclusions. Scientific novelty is the development of an effective method of image classification based on the introduction of a system of probability distributions for data components, which contributes to in-depth analysis in ...
وصف الملف: application/pdf
العلاقة: http://ais.khpi.edu.ua/article/view/226801/226363Test; http://ais.khpi.edu.ua/article/view/226801Test
الإتاحة: https://doi.org/10.20998/2522-9052.2021.1.01Test
http://ais.khpi.edu.ua/article/view/226801Test
