Classification of Images Based on a System of Hierarchical Features

被引:5
|
作者
Daradkeh, Yousef Ibrahim [1 ]
Gorokhovatskyi, Volodymyr [2 ]
Tvoroshenko, Iryna [2 ]
Al-Dhaifallah, Mujahed [3 ,4 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Coll Engn Wadi Addawasir, Dept Comp Engn & Networks, Al Kharj 11991, Saudi Arabia
[2] Kharkiv Natl Univ Radio Elect, Dept Informat, UA-61166 Kharkiv, Ukraine
[3] King Fand Univ Petr & Minerals, Control & Instrumentat Engn Dept, Dhahran 31261, Saudi Arabia
[4] King Fand Univ Petr & Minerals, Interdisciplinary Res Ctr IRC Renewable Energy &, Dhahran 31261, Saudi Arabia
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2022年 / 72卷 / 01期
关键词
Bitwise distribution; computer vision; descriptor; hierarchical rep-; resentation; image classification; keypoint; noise immunity; processing speed; ALGORITHM; MODEL;
D O I
10.32604/cmc.2022.025499
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1785 / 1797
页数:13
相关论文
共 50 条
  • [1] FFT features and hierarchical classification algorithms for cloud images
    Kliangsuwan, Thitinan
    Heednacram, Apichat
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2018, 76 : 40 - 54
  • [2] Classification of Liver Images Based on a Feedback Hierarchical "Support Vector Machines" System
    Jiang, Huiyan
    Liu, Xiangying
    INTERNATIONAL JOURNAL OF OBESITY, 2011, 35 : S57 - S57
  • [3] Video-Based Classification of Driving Behavior Using a Hierarchical Classification System with Multiple Features
    Yan, Chao
    Coenen, Frans
    Yue, Yong
    Yang, Xiaosong
    Zhang, Bailing
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2016, 30 (05)
  • [4] WaveLBP based hierarchical features for image classification
    Song, Tiecheng
    Li, Hongliang
    PATTERN RECOGNITION LETTERS, 2013, 34 (12) : 1323 - 1328
  • [5] Content-based hierarchical classification of vacation images
    Vailaya, A
    Figueiredo, M
    Jain, A
    Zhang, HJ
    IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS, PROCEEDINGS VOL 1, 1999, : 518 - 523
  • [6] Content-based hierarchical classification of vacation images
    Vailaya, Aditya
    Figueiredo, Mario
    Jain, Anil
    Zhang, HongJiang
    International Conference on Multimedia Computing and Systems -Proceedings, 1999, 1 : 518 - 523
  • [7] Classification of farmland images based on color features
    Miao, Rong-Hui
    Tang, Jing-Lei
    Chen, Xiao-Qian
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2015, 29 : 138 - 146
  • [8] Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification
    Zhao, Yang
    Liang, Jiaqi
    Huang, Sisi
    Huang, Pingping
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 9442 - 9450
  • [9] Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata
    Espinola, Moises
    Piedra-Fernandez, Jose A.
    Ayala, Rosa
    Iribarne, Luis
    Wang, James Z.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (02): : 795 - 809
  • [10] Wavelet Based Features For Ultrasound Placenta Images Classification
    Malathi, G.
    Shanthi, V.
    2009 SECOND INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING AND TECHNOLOGY (ICETET 2009), 2009, : 751 - +