Statistical Methods for Analyzing and Processing Data Components When Recognizing Visual Objects in the Space of Key Point Descriptors

Gorokhovatskyi, Volodymyr; Gadetska, Svitlana; Gorokhovatskyi, Oleksii; Ponomarenko, Roman

doi:10.1007/978-3-030-61656-4_15

Cited by 1 publication

(4 citation statements)

References 17 publications

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“…2) By assigning individually each of the object descriptors to one of the etalons with the determination of the etalon that received the largest number of class votes [7], [11], [37]. 3) Using the apparatus of statistical data analysis to make decision H about a significant difference between the distributions of data for describing the object and etalons with the definition of the etalon, which has a non-significant difference [8], [33], [35]. With this approach, according to the apparatus of statistical testing of hypothesis )] , ( ), , ( […”

Section: Methods Of Constructing Classifiers According To Transformed...mentioning

confidence: 99%

“…The variants of implementing the third method are implemented, for example, by comparing the distributions of object data and etalons through the application of a twosample t -test for meansboth for bit-by-bit comparison of the vectors, which are integrated representations of the objects and for comparing such vectors as a whole [33], [35].…”

Section: Methods Of Constructing Classifiers According To Transformed...mentioning

confidence: 99%

“…1) The construction and use of data distributions instead of data values significantly reduce the number of calculations [2], [33].…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Such approaches by generalizing data transformation significantly simplify and speed up classification. At the same time, it is effective to combine methods and implement a complex of approaches to improve performance, namely: reducing the number of descriptors in the description [37], statistical processing with the identification of the most informative components [16], [29], [30], [33], [35], [37]. The possibility of implementing training procedures in such classifiers, where etalon descriptions of classes are considered to be given, also contributes to further improvement of their performance.…”

Section: Review Of the Literaturementioning

confidence: 99%

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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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Section: Methods Of Constructing Classifiers According To Transformed...mentioning

confidence: 99%

Section: Methods Of Constructing Classifiers According To Transformed...mentioning

confidence: 99%

“…1) The construction and use of data distributions instead of data values significantly reduce the number of calculations [2], [33].…”

Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

See 2 more Smart Citations

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

Daradkeh,

Gorokhovatskyi,

Tvoroshenko

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

Statistical Methods for Analyzing and Processing Data Components When Recognizing Visual Objects in the Space of Key Point Descriptors

Cited by 1 publication

References 17 publications

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

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

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