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Image annotation via graph learning
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DOI：10.1016/j.patcog.2008.04.012.png)
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摘要
En 中文
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. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Graph learning
Image annotation
Image similarity
Word correlation
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
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