Name
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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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3307363.3307387 | Proceedings of the 11th International Conference on Computer Modeling and Simulation |
Keywords | DocType | ISBN |
image object recognition, ontology | Conference | 978-1-4503-6619-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheyuan Ding | 1 | 4 | 1.75 |
| Li Yao | 2 | 7 | 2.17 |
| Bin Liu | 3 | 10 | 3.53 |
| Junfeng Wu | 4 | 5 | 2.77 |