research-article

Review of the Application of Ontology in the Field of Image Object Recognition

Authors:

Zheyuan Ding,

Li Yao,

Bin Liu,

Junfeng WuAuthors Info & Claims

ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation

Pages 142 - 146

https://doi.org/10.1145/3307363.3307387

Published: 16 January 2019 Publication History

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Abstract

Image object recognition is an important research field in computer vision. It has a wide application prospect and practical significance in the information age. Although the current image recognition technology has achieved high accuracy in some tasks, the computer has many deficiencies in the automatic recognition of images such as fine-grained recognition, recognition of complex scenes. In these tasks, some issues exist like insufficient precision, complex high-level semantics which is difficult to identify and so on. This paper reviews the application of ontology in image object recognition. It is found that combining ontology knowledge model and traditional image recognition technology can improve recognition accuracy, enhance high-level semantic recognition ability, reduce the demand of the large number of training samples, and improve the scalability of the image recognition system. Otherwise, this paper also summarizes the frontier research of ontology applied in the field of image object recognition and the difficulties of deep integration of different technologies and ontology.

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Cited By

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Dubovskoi ABaimuratov ISokolov M(2024)Informed Object Detection for Computer GamesPattern Recognition and Image Analysis10.1134/S105466182470070634:3(810-818)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1134/S1054661824700706
Monka SHalilaj LRettinger AAlam MBuscaldi DCochez MOsborne FRefogiato Recupero DSack H(2022)A survey on visual transfer learning using knowledge graphsSemantic Web10.3233/SW-21295913:3(477-510)Online publication date: 6-Apr-2022
https://dl.acm.org/doi/10.3233/SW-212959
Belkebir MMaarouk TNini B(2022)Integrating Ontology with Imaging and Artificial Vision for a High-Level Semantic: A ReviewProceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems10.1007/978-3-031-20429-6_4(32-41)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-20429-6_4
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Index Terms

Review of the Application of Ontology in the Field of Image Object Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation

January 2019

253 pages

ISBN:9781450366199

DOI:10.1145/3307363

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Wollongong, Australia
College of Technology Management, National Tsing Hua University, Taiwan
Swinburne University of Technology
University of Technology Sydney

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Association for Computing Machinery

New York, NY, United States

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Published: 16 January 2019

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ICCMS 2019: The 11th International Conference on Computer Modeling and Simulation

January 16 - 19, 2019

QLD, North Rockhampton, Australia

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Dubovskoi ABaimuratov ISokolov M(2024)Informed Object Detection for Computer GamesPattern Recognition and Image Analysis10.1134/S105466182470070634:3(810-818)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1134/S1054661824700706
Monka SHalilaj LRettinger AAlam MBuscaldi DCochez MOsborne FRefogiato Recupero DSack H(2022)A survey on visual transfer learning using knowledge graphsSemantic Web10.3233/SW-21295913:3(477-510)Online publication date: 6-Apr-2022
https://dl.acm.org/doi/10.3233/SW-212959
Belkebir MMaarouk TNini B(2022)Integrating Ontology with Imaging and Artificial Vision for a High-Level Semantic: A ReviewProceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems10.1007/978-3-031-20429-6_4(32-41)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-20429-6_4
Bourguin GLewandowski ABouneffa MAhmad A(2021)Towards Ontologically Explainable ClassifiersArtificial Neural Networks and Machine Learning – ICANN 202110.1007/978-3-030-86340-1_38(472-484)Online publication date: 14-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-86340-1_38

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Image ratio features for facial expression recognition application

Image Semantics Recognition Based on Ontology and Description Logics

Homotopic Image Pseudo-Invariants for Openset Object Recognition and Image Retrieval

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