The results of the development of the new fast-speed method of classification images using a structural approach are presented.The method is based on the system of hierarchical features,based on the bitwise data distr...The results of the development of the new fast-speed method of classification images using a structural approach are presented.The method is based on the system of hierarchical features,based on the bitwise data distribution for the set of descriptors of image description.The article also proposes the use of the spatial data processing apparatus,which simplifies and accelerates the classification process.Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure,for which the sets of descriptors are compared.The introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of classification.The noise immunity of the method to additive noise has been experimentally studied.According to the results of the research,the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit distribution.Computing costs increase proportionally with decreasing bit distribution.The method can be used for application tasks where object identification time is critical.展开更多
The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of ...The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.展开更多
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 seg...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 developedmethods is hundreds of times less than according to the traditional one,without reducing the accuracy.展开更多
文摘The results of the development of the new fast-speed method of classification images using a structural approach are presented.The method is based on the system of hierarchical features,based on the bitwise data distribution for the set of descriptors of image description.The article also proposes the use of the spatial data processing apparatus,which simplifies and accelerates the classification process.Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure,for which the sets of descriptors are compared.The introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of classification.The noise immunity of the method to additive noise has been experimentally studied.According to the results of the research,the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit distribution.Computing costs increase proportionally with decreasing bit distribution.The method can be used for application tasks where object identification time is critical.
基金This research was funded by Prince Sattam bin Abdulaziz University(Project Number PSAU/2023/01/25387).
文摘The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.
基金The authors received specific funding for this research-Project Number IF-PSAU-2021/01/18487.
文摘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 developedmethods is hundreds of times less than according to the traditional one,without reducing the accuracy.
