An Adaptive Image Content Representation and Segmentation Approach to Automatic Image Annotation
An Adaptive Image Content Representation and Segmentation Approach to Automatic Image Annotation
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DOI：
10.1007/978-3-540-27814-6_64
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发表时间：
2004-07
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影响因子：
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通讯作者：
Rui Shi;Huamin Feng;Tat-Seng Chua;Chin-Hui Lee
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文献类型：
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作者：
Rui Shi;Huamin Feng;Tat-Seng Chua;Chin-Hui Lee
图像自动标注是近年来基于内容的图像检索研究的热点。在本文中，我们提出了一种新的方法来自动图像标注的基础上的两个关键组成部分：（a）一个自适应的视觉特征表示的图像内容的匹配追踪算法的基础上;（B）一个自适应的两级分割方法。它们用于解决将图像分割成有意义的单元，并用有区别的视觉特征表示每个单元的内容的重要问题。使用一组约800个训练和测试图像，我们比较这些技术在图像检索对其他流行的分割方案，和传统的非自适应特征表示方法。我们的初步结果表明，该方法优于其他竞争系统的基础上流行的Blobworld分割方案和其他流行的特征表示方法，如DCT和小波。特别是，我们的系统实现了超过50%的F1测量图像注释任务。
Automatic image annotation has been intensively studied for content-based image retrieval recently. In this paper, we propose a novel approach to automatic image annotation based on two key components: (a) an adaptive visual feature representation of image contents based on matching pursuit algorithms; and (b) an adaptive two-level segmentation method. They are used to address the important issues of segmenting images into meaningful units, and representing the contents of each unit with discriminative visual features. Using a set of about 800 training and testing images, we compare these techniques in image retrieval against other popular segmentation schemes, and traditional non-adaptive feature representation methods. Our preliminary results indicate that the proposed approach outperforms other competing systems based on the popular Blobworld segmentation scheme and other prevailing feature representation methods, such as DCT and wavelets. In particular, our system achieves an F1 measure of over 50% for the image annotation task.