High-level semantic based image characterization using Artificial Neural Networks

被引:0
|
作者
Ribeiro, Eduardo Ferreira [1 ]
Barcelos, Celia Aparecida Zorzo [1 ,2 ]
Batista, Marcos Aurelio [3 ]
机构
[1] Univ Fed Uberlandia, Fac Ciencia Comp, Av Engenheiro Dinz 1178,CP 593, BR-38400 Uberlandia, MG, Brazil
[2] Univ Fed Uberlandia, Fac Matemat, Uberlandia, MG, Brazil
[3] Univ Fed Goias, Dept Ciencia Comp, Catalao, Go, Brazil
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Low-level attributes such as color shape and texture generally fail in describing the high-level. semantic concepts. This work presents, through the formation of a high-level characteristics vector the representation of the subjective knowledge used by humans for the, verification of which aspects are most important for image characterization. Such vector will be formed by using the Artificial Intelligence techniques, more specifically the Artificial Neural Networks, which will generate, through predefined examples, the low-level characteristics forming the new high-level vector making image retrieval possible. Finally, some tests results are presented and discussed to demonstrate the potentiality of the method.
引用
收藏
页码:357 / +
页数:2
相关论文
共 50 条
  • [1] Image enhancemen by deep neural networks using high-level information
    Titarenko, M. A.
    Malashin, R. O.
    JOURNAL OF OPTICAL TECHNOLOGY, 2020, 87 (10) : 604 - 610
  • [2] High-Level Design of Sigma-Delta Modulators using Artificial Neural Networks
    Diaz-Lobo, Pablo
    de la Rosa, Jose M.
    2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS, 2023,
  • [3] Algorithms of high-level semantic-based image retrieval
    Wang, Chong-Jun
    Yang, Yu-Bin
    Chen, Shi-Fu
    Ruan Jian Xue Bao/Journal of Software, 2004, 15 (10): : 1461 - 1469
  • [4] On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators
    Diaz-Lobo, Pablo
    Linan-Cembrano, Gustavo
    de la Rosa, Jose M.
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2024, 71 (05) : 2006 - 2016
  • [5] Research on High-Level Semantic Image Retrieval
    Ri, Chang-Yong
    Yao, Min
    COMPUTATIONAL MATERIALS SCIENCE, PTS 1-3, 2011, 268-270 : 1427 - +
  • [6] HIGH-LEVEL SEMANTIC PHOTOGRAPHIC COMPOSITION ANALYSIS AND UNDERSTANDING WITH DEEP NEURAL NETWORKS
    Wu, Min-Tzu
    Pan, Tse-Yu
    Tsai, Wan-Lun
    Kuo, Hsu-Chan
    Hu, Min-Chun
    2017 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW), 2017,
  • [7] High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks
    Alejandro Martinez-Nieto, Javier
    Medrano-Marques, Nicolas
    Teresa Sanz-Pascual, Maria
    Calvo-Lopez, Belen
    SENSORS, 2019, 19 (08)
  • [8] High-level semantic image annotation based on hot Internet topics
    Wang, Xiaoru
    Du, Junping
    Wu, Shuzhe
    Li, Xu
    Xin, Haiming
    Zhang, Yu
    Li, Fu
    MULTIMEDIA TOOLS AND APPLICATIONS, 2015, 74 (06) : 2055 - 2084
  • [9] High-level semantic image annotation based on hot Internet topics
    Xiaoru Wang
    Junping Du
    Shuzhe Wu
    Xu Li
    Haiming Xin
    Yu Zhang
    Fu Li
    Multimedia Tools and Applications, 2015, 74 : 2055 - 2084
  • [10] Indoor Image Representation by High-Level Semantic Features
    Sitaula, Chiranjibi
    Xiang, Yong
    Zhang, Yushu
    Lu, Xuequan
    Aryal, Sunil
    IEEE ACCESS, 2019, 7 : 84967 - 84979