{"title":"分类结构方法中的图像描述压缩","authors":"Volodymyr Gorokhovatskyi;Iryna Tvoroshenko;Olena Yakovleva;Monika Hudáková","doi":"10.1109/ACCESS.2025.3548910","DOIUrl":null,"url":null,"abstract":"The problem solved in the article is reduction of computational costs for the image classification process when applying structural methods. The main focus is implementing tools for granulation, screening, and clustering processing a set of elements of etalon descriptions. As a result of compression, each etalon is transformed into a reduced set of descriptors or data centroids, ensuring high speed and performance of image classification. Several variants of simple data compression schemes are assessed and compared to the traditional linear search method, along with two variants of etalon clustering. The comparison includes results achieved for the entire data set and for each of the images separately. The paper presents the results of software modeling of the proposed approaches for two experimental sets containing images of football club logos and artistic paintings. The test sample includes a set of images from the etalon database along with other images that do not belong to the database and with a set of geometric transformations of shift, scale, and rotation in the field of view applied to them. The research covers practical issues of choosing threshold parameters to set the equivalence of descriptors and minimizing the number of class votes to ensure the required level of classification accuracy. Testing has confirmed a significant processing acceleration and a sufficiently increasing level of classification accuracy due to employing compression. Particularly the conducted modeling revealed a tenfold increase in speed. It has been experimentally confirmed that using a clustering apparatus has a much higher potential in terms of classification accuracy and speed than simple sifting or granulation schemes based on close description components.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"43631-43641"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10915635","citationCount":"0","resultStr":"{\"title\":\"Image Description Compression in Classification Structural Methods\",\"authors\":\"Volodymyr Gorokhovatskyi;Iryna Tvoroshenko;Olena Yakovleva;Monika Hudáková\",\"doi\":\"10.1109/ACCESS.2025.3548910\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem solved in the article is reduction of computational costs for the image classification process when applying structural methods. The main focus is implementing tools for granulation, screening, and clustering processing a set of elements of etalon descriptions. As a result of compression, each etalon is transformed into a reduced set of descriptors or data centroids, ensuring high speed and performance of image classification. Several variants of simple data compression schemes are assessed and compared to the traditional linear search method, along with two variants of etalon clustering. The comparison includes results achieved for the entire data set and for each of the images separately. The paper presents the results of software modeling of the proposed approaches for two experimental sets containing images of football club logos and artistic paintings. The test sample includes a set of images from the etalon database along with other images that do not belong to the database and with a set of geometric transformations of shift, scale, and rotation in the field of view applied to them. The research covers practical issues of choosing threshold parameters to set the equivalence of descriptors and minimizing the number of class votes to ensure the required level of classification accuracy. Testing has confirmed a significant processing acceleration and a sufficiently increasing level of classification accuracy due to employing compression. Particularly the conducted modeling revealed a tenfold increase in speed. It has been experimentally confirmed that using a clustering apparatus has a much higher potential in terms of classification accuracy and speed than simple sifting or granulation schemes based on close description components.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":\"13 \",\"pages\":\"43631-43641\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10915635\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10915635/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10915635/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Image Description Compression in Classification Structural Methods
The problem solved in the article is reduction of computational costs for the image classification process when applying structural methods. The main focus is implementing tools for granulation, screening, and clustering processing a set of elements of etalon descriptions. As a result of compression, each etalon is transformed into a reduced set of descriptors or data centroids, ensuring high speed and performance of image classification. Several variants of simple data compression schemes are assessed and compared to the traditional linear search method, along with two variants of etalon clustering. The comparison includes results achieved for the entire data set and for each of the images separately. The paper presents the results of software modeling of the proposed approaches for two experimental sets containing images of football club logos and artistic paintings. The test sample includes a set of images from the etalon database along with other images that do not belong to the database and with a set of geometric transformations of shift, scale, and rotation in the field of view applied to them. The research covers practical issues of choosing threshold parameters to set the equivalence of descriptors and minimizing the number of class votes to ensure the required level of classification accuracy. Testing has confirmed a significant processing acceleration and a sufficiently increasing level of classification accuracy due to employing compression. Particularly the conducted modeling revealed a tenfold increase in speed. It has been experimentally confirmed that using a clustering apparatus has a much higher potential in terms of classification accuracy and speed than simple sifting or granulation schemes based on close description components.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.