Name
Abstract | ||
|---|---|---|
Image annotation is an open and challenging task, especially with large label vocabulary. In this paper, we propose a novel graph learning based method for image annotation, which takes both advantages of the nearest neighbour based and the graph-based methods, by exploiting the graph learning method to propagate the labels on the graph corresponding to the K nearest neighbours of a test image. To acquire more effective graph weights for computing score for each label, besides the similarity of visual features, our method also considers the similarity of two label sets, which is computed based on the label correlation that captures the semantic information between two labels. In addition, we combine the image-to-label distance with the graph learning based score to compute the final decision value for labelling. The proposed method is evaluated on three benchmark datasets for image annotation. The result shows our method substantially outperforms the previous graph learning based methods, and our result matches the current state-of-the-art results in annotation quality.
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Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2671188.2749383 | ICMR '15: International Conference on Multimedia Retrieval
Shanghai
China
June, 2015 |
Keywords | Field | DocType |
Image annotation, graph learning, label correlation, k nearest neighbours, image-to-label distance | Data mining,Computer science,Nearest neighbor graph,Artificial intelligence,Standard test image,Graph,Graph database,Automatic image annotation,Annotation,Pattern recognition,Correlation,Vocabulary,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-3274-3 | 5 | 0.41 |
References | Authors | |
24 | 2 | |