Image annotation via graph learning

  • Authors:

  • Jing Liu;Mingjing Li;Qingshan Liu;Hanqing Lu;Songde Ma

  • Affiliations:

  • Institute of Automation Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Beijing 100080, China;Microsoft Research Asia, Beijing 100080, China;Institute of Automation Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Beijing 100080, China;Institute of Automation Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Beijing 100080, China;Institute of Automation Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Beijing 100080, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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Abstract

Image annotation has been an active research topic in recent years due to its potential impact on both image understanding and web image search. In this paper, we propose a graph learning framework for image annotation. First, the image-based graph learning is performed to obtain the candidate annotations for each image. In order to capture the complex distribution of image data, we propose a Nearest Spanning Chain (NSC) method to construct the image-based graph, whose edge-weights are derived from the chain-wise statistical information instead of the traditional pairwise similarities. Second, the word-based graph learning is developed to refine the relationships between images and words to get final annotations for each image. To enrich the representation of the word-based graph, we design two types of word correlations based on web search results besides the word co-occurrence in the training set. The effectiveness of the proposed solution is demonstrated from the experiments on the Corel dataset and a web image dataset.