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Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering

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  • Zhangang Hao
  • Hongwei Ge
  • Tianpeng Gu

Abstract

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.

Suggested Citation

  • Zhangang Hao & Hongwei Ge & Tianpeng Gu, 2017. "Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-11, May.
    • Handle: RePEc:hin:jnlmpe:8493267
    DOI: 10.1155/2017/8493267
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