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
  • [21] Integrating Ontology with Imaging and Artificial Vision for a High-Level Semantic: A Review
    Belkebir, Malak
    Maarouk, Toufik Messaoud
    Nini, Brahim
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND INTELLIGENT SYSTEMS, ICETIS 2022, VOL 2, 2023, 573 : 32 - 41
  • [22] Using high-level semantic features in video retrieval
    Zheng, Wujie
    Li, Jianmin
    Si, Zhangzhang
    Lin, Fuzong
    Zhang, Bo
    IMAGE AND VIDEO RETRIEVAL, PROCEEDINGS, 2006, 4071 : 370 - 379
  • [23] A high-level tool for the development of FPLD-based stochastic neural networks
    Maunder, B
    Salcic, Z
    Coghill, G
    ICICS - PROCEEDINGS OF 1997 INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATIONS AND SIGNAL PROCESSING, VOLS 1-3: THEME: TRENDS IN INFORMATION SYSTEMS ENGINEERING AND WIRELESS MULTIMEDIA COMMUNICATIONS, 1997, : 684 - 688
  • [24] Cognitive computational semantic for high resolution image interpretation using artificial neural network
    Minu, R., I
    Nagarajan, G.
    Suresh, A.
    Devi, Jayanthila A.
    BIOMEDICAL RESEARCH-INDIA, 2016, 27 : S306 - S309
  • [25] Learning a Smart Convolutional Neural Network with High-level Semantic Information
    Qiao, Xinshu
    Xu, Chunyan
    Yang, Jian
    Jiang, Jiatao
    PROCEEDINGS 2017 4TH IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION (ACPR), 2017, : 190 - 195
  • [26] Mapping low-level features to high-level semantic concepts in region-based image retrieval
    Jiang, W
    Chan, KL
    Li, MJ
    Zhang, HJ
    2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 2005, : 244 - 249
  • [27] High-Level Hierarchical Semantic Processing Framework for Smart Sensor Networks
    Bruckner, Dietmar
    Kasbi, Jamal
    Velik, Rosemarie
    Herzner, Wolfgang
    2008 CONFERENCE ON HUMAN SYSTEM INTERACTIONS, VOLS 1 AND 2, 2008, : 674 - +
  • [28] High-Level Semantic Networks for Multi-Scale Object Detection
    Cao, Jiale
    Pang, Yanwei
    Zhao, Shengjie
    Li, Xuelong
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 30 (10) : 3372 - 3386
  • [29] LEARNING HIGH-LEVEL BEHAVIORS FROM DEMONSTRATION THROUGH SEMANTIC NETWORKS
    Fonooni, Benjamin
    Hellstrom, Thomas
    Janlert, Lars-Erik
    ICAART: PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1, 2012, : 419 - 426
  • [30] High-Level Hierarchical Semantic Processing Framework for Smart Sensor Networks
    Bruckner, D.
    Picus, C.
    Velik, R.
    Herzner, W.
    Zucker, G.
    HUMAN-COMPUTER SYSTEMS INTERACTION: BACKGROUNDS AND APPLICATIONS, 2009, 60 : 347 - +