A learning-based approach for annotating large on-line image collection
A learning-based approach for annotating large on-line image collection
复制标题
一种基于学习的方法来注释大型在线图像集合
DOI：
10.1109/mulmm.2004.1264993
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Tat
中科院分区：
文献类型：
--
作者：
Huamin Feng;Tat
最近的一些工作试图通过利用分割图像特征提供的视觉信息和关联文本提供的语义概念之间的链接来自动标注图像集合。然而，这种方法的主要局限性是，通常不能进行语义上有意义的分割。本文提出了一种新的基于统计学习的方法来克服这一问题。我们使用两种不同的分割方法将图像分割成两组区域，并学习每组区域与文本概念之间的关联。给出一个新的图像，这个想法是首先采用贪婪的策略，用来自不同重叠和可能冲突区域集的概念来注释图像。然后，我们结合一个决策模型来消除使用重叠区域的视觉特征学习的概念的歧义。在一个中等大小的图像集上的实验表明，与只使用一种分割方法的系统相比，使用我们的消歧方法可以使系统的F/sub1/measures平均提高约12%-16%。
Several recent works attempt to automatically annotate image collection by exploiting the links between visual information provided by segmented image features and semantic concepts provided by associated text. The main limitation of such approaches, however, is that semantically meaningful segmentation is in general unavailable. This paper proposes a novel statistical learning-based approach to overcome this problem. We employ two different segmentation methods to segment the image into two sets of regions and learn the association between each set of regions with text concepts. Given a new image, the idea is to first employ a greedy strategy to annotate the image with concepts derived from different sets of overlapping and possibly conflicting regions. We then incorporate a decision model to disambiguate the concepts learned using the visual features of the overlapping regions. Experiments on a mid-sized image collection demonstrate that the use of our disambiguation approach could improve the performance of the system by about 12-16% on average in terms of F/sub 1/ measures as compared to system that uses only one segmentation method.