کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
531521 | 869850 | 2009 | 11 صفحه PDF | دانلود رایگان |
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.
Journal: Pattern Recognition - Volume 42, Issue 2, February 2009, Pages 218–228