Automatic image tagging via category label and web data
Automatic image tagging via category label and web data
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DOI：
10.1145/1873951.1874164
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Shenghua Gao;Zhengxiang Wang-;L. Chia;I. Tsang
中科院分区：
文献类型：
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作者：
Shenghua Gao;Zhengxiang Wang-;L. Chia;I. Tsang
图像标记是图像内容理解和基于文本的图像处理的重要技术。给定一组图像，如何高效地标记这些图像是一个有趣的问题。本文提出了一种新的半自动图像标记技术：首先为每幅图像分配一个类别标签，然后利用现有的海量Web数据，自动为每幅图像推荐那些有希望的标签。本文的主要贡献可以突出如下：（i）通过为每个图像分配类别标签，我们的方法可以自动为图像推荐其他标签，从而减少人工注释工作。同时，由于Web数据的丰富性，我们的方法保证了标签的多样性。(ii)我们使用稀疏编码来自动选择那些语义相关的图像进行标签传播。(iii)本地和全球排名聚集将使我们的方法对噪声标签具有鲁棒性。我们使用事件数据集作为待标记的图像，并根据事件数据集中的类别标签抓取Flickr图像及其相关标签作为辅助Web数据。实验结果表明，该方法在图像标注中取得了良好的效果，证明了该方法的有效性。
Image tagging is an important technique for the image content understanding and text based image processing. Given a selection of images, how to tag these images efficiently and effectively is an interesting problem. In this paper, a novel semi-auto image tagging technique is proposed: By assigning each image a category label first, our method can automatically recommend those promising tags to each image by utilizing existing vast web data. The main contributions of our paper can be highlighted as follows: (i) By assigning each image a category label, our method can automatically recommend other tags to the image, thus reducing the human annotation efforts. Meanwhile, our method guarantee tags' diversity due to abundant web data. (ii) We use sparse coding to automatically select those semantically related images for tag propagation. (iii) Local & global ranking agglomeration will make our method robust to noisy tags. We use Event dataset as the images to be tagged, and crawled Flickr images with their associated tags according to the category label in Event dataset as the auxiliary web data. Experimental results show that our method achieves promising performance for image tagging, which proves the effectiveness of our method.