Automatic image annotation by using relevant keywords extracted from auxiliary text documents

  • Authors:

  • Ning Zhou;Yi Shen;Jianping Fan

  • Affiliations:

  • University of North Carolina - Charlotte, Charlotte, NC, USA;University of North Carolina - Charlotte, Charlotte, NC, USA;University of North Carolina - Charlotte, Charlotte, NC, USA

  • Venue:
  • Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
  • Year:
  • 2010

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Abstract

In this paper, a novel algorithm is developed to enable automatic image annotation by aligning web images with their most relevant auxiliary text terms. First, large-scale web pages are crawled and automatic web page segmentation is performed to extract informative images and their most relevant auxiliary text blocks. Second, image clustering is performed to partition the web images into a set of image clusters according to their visual similarity contexts. By grouping the web images according to their common visual properties, the uncertainty of the relatedness between the web images and their auxiliary text terms is significantly reduced. Finally, a relevance re-ranking algorithm is developed to achieve more precise alignment between the web images with their most relevant auxiliary text terms. Our experiments on large-scale web pages have provided very positive results.