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

Civil Engineering

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