Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering
Joint Authors
Hao, Zhangang
Ge, Hongwei
Gu, Tianpeng
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-18
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
With the progress of network technology, there are more and more digital images of the internet.
But most images are not semantically marked, which makes it difficult to retrieve and use.
In this paper, a new algorithm is proposed to automatically annotate images based on particle swarm optimization (PSO) and support vector clustering (SVC).
The algorithm includes two stages: firstly, PSO algorithm is used to optimize SVC; secondly, the trained SVC algorithm is used to annotate the image automatically.
In the experiment, three datasets are used to evaluate the algorithm, and the results show the effectiveness of the algorithm.
American Psychological Association (APA)
Hao, Zhangang& Ge, Hongwei& Gu, Tianpeng. 2017. Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1192369
Modern Language Association (MLA)
Hao, Zhangang…[et al.]. Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering. Mathematical Problems in Engineering No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1192369
American Medical Association (AMA)
Hao, Zhangang& Ge, Hongwei& Gu, Tianpeng. Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1192369
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192369