scholarly journals Application of data hashing tools to accelerate classification decisions in structural image recognition methods

2021 ◽  
Vol 5 (2) ◽  
pp. 13-20
Author(s):  
Volodymyr Gorokhovatsky ◽  
Nataliia Vlasenko ◽  
Mykhailo Rybalka
Keyword(s):  
Image Classification ◽  
High Speed ◽  
Data Representation ◽  
Practical Significance ◽  
Classification Methods ◽  
Software Modeling ◽  
Space Efficiency ◽  
Software Models ◽  
Applied Software ◽  
Structural Image

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.

scholarly journals THE SPEED CALCULATING INCREASING METHOD OF THE MARKOV MODEL NETWORK NODE

2021 ◽  
Vol 5 (3) ◽  
pp. 13-17
Author(s):  
Pavlo Pustovoitov ◽  
Maxim Okhrimenko ◽  
Vitalii Voronets ◽  
Dmitry Udalov
Keyword(s):  
Image Classification ◽  
High Speed ◽  
Data Representation ◽  
Practical Significance ◽  
Classification Methods ◽  
Software Modeling ◽  
Space Efficiency ◽  
Software Models ◽  
Applied Software ◽  
Key Points

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.

scholarly journals The image description reduction in the set of descriptors on informativeness metric criteria base

2021 ◽  
Vol 5 (4) ◽  
pp. 10-16
Author(s):  
Volodymyr Gorokhovatskyi ◽  
Nataliia Vlasenko
Keyword(s):  
Computer Vision ◽  
Image Classification ◽  
Computing Time ◽  
Data Representation ◽  
Practical Significance ◽  
Classification Methods ◽  
Vision Systems ◽  
Software Models ◽  
Reference Images ◽  
Computer Vision Systems

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.

scholarly journals Combination method of accelerated metric data search in image classification problems

2021 ◽  
Vol 5 (3) ◽  
pp. 5-12
Author(s):  
Volodymyr Gorokhovatsky ◽  
Natalia Stiahlyk ◽  
Vytaliia Tsarevska
Keyword(s):  
Computer Vision ◽  
Image Classification ◽  
Computing Time ◽  
Combination Method ◽  
Practical Significance ◽  
Classification Methods ◽  
Vision Systems ◽  
Software Models ◽  
Reference Images ◽  
Computer Vision Systems

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.

scholarly journals Multimedia Teaching Platform for Urban Planning Utilizing 3D Technology

2018 ◽  
Vol 13 (04) ◽  
pp. 187 ◽  
Author(s):  
Xuhui Wang ◽  
Quan Zhang ◽  
Yanyi Chen ◽  
Shihao Liang
Keyword(s):  
High Speed ◽  
Three Dimensional ◽  
Practical Significance ◽  
Master Plan ◽  
3D Technology ◽  
Technology Application ◽  
Teaching Platform ◽  
Multimedia Teaching ◽  
Effect Difference ◽  
Urban Rural

In recent years, 3D technology based on computer and internet has achieved high-speed development. People have realized direct and stereo observation of realistic world. Three-dimensional and visualized characteristics of the technology fit well with the teaching objective of college architecture specialized courses. Thus, 3D model has profound practical significance for its application in urban green space system and urban rural overall planning. With “urban-rural master plan” as experimental course, through design of “urban-rural master plan” multimedia teaching platform based on 3D technology and practice of the teaching platform in course teaching, this article has applied control experiment method and statistical method to make comparative analysis on the teaching effect difference of multimedia teaching platform based on 3D technology application in “urban-rural master plan” as experimental course so as to provide theoretical and data support for 3D technology application in “urban-rural master plan” and other college architecture major courses.

scholarly journals Effective combining of color and texture descriptors for indoor-outdoor image classification

2014 ◽  
Vol 27 (3) ◽  
pp. 399-410 ◽  
Author(s):  
Stevica Cvetkovic ◽  
Sasa Nikolic ◽  
Slobodan Ilic
Keyword(s):  
Image Classification ◽  
Accurate Method ◽  
Optimal Combination ◽  
Compact Representation ◽  
Svm Classifier ◽  
Classification Methods ◽  
Empirical Tests ◽  
Public Datasets ◽  
Fast Extraction ◽  
Classification Procedures

Although many indoor-outdoor image classification methods have been proposed in the literature, most of them have omitted comparison with basic methods to justify the need for complex feature extraction and classification procedures. In this paper we propose a relatively simple but highly accurate method for indoor-outdoor image classification, based on combination of carefully engineered MPEG-7 color and texture descriptors. In order to determine the optimal combination of descriptors in terms of fast extraction, compact representation and high accuracy, we conducted comprehensive empirical tests over several color and texture descriptors. The descriptors combination was used for training and testing of a binary SVM classifier. We have shown that the proper descriptors preprocessing before SVM classification has significant impact on the final result. Comprehensive experimental evaluation shows that the proposed method outperforms several more complex indoor-outdoor image classification techniques on a couple of public datasets.

scholarly journals Extracting image features for classification by two-tier genetic programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
A Song ◽  
K Neshatian ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Process ◽  
High Accuracy ◽  
Classification Performance ◽  
Image Features ◽  
Classification Methods ◽  
Feature Based ◽  
Second Tier

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.

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