Retrieving lightly annotated images using image similarities
Retrieving lightly annotated images using image similarities
复制标题
使用图像相似度检索轻微注释的图像
DOI：
10.1145/1066677.1066914
复制
发表时间：
2005
期刊：
影响因子：
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通讯作者：
N. Ueda
中科院分区：
文献类型：
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作者：
Masashi Inoue;N. Ueda
用户的搜索需求通常由单词表示，并且根据这样的文本查询来检索图像。分配给存储图像的注释词对于将查询连接到图像是最有用的。然而，由于注释成本，在许多情况下可获得的注释词的数量非常有限。当根本没有给出注释时，需要一些自动分配注释的技术。当每个图像只有几个注释单词时（轻度注释），需要一些最好地使用可用注释的增强技术。我们解决后一个问题，估计词的关联，以填补查询和注释之间的词汇差距。词汇联想的模型可以从数据中学习。然而，由于图像只是轻微的注释，它们在计算单词关联时的稀疏性变得至关重要。为了补偿稀疏性，我们提出了一种新的数据探索技术，在该技术中，图像相似性有助于估计的词关联的假设，相似的图像具有相似的语义概念。我们的实验表明，我们的方法的潜在好处。
Users' search needs are often represented by words and images are retrieved according to such textual queries. Annotation words assigned to the stored images are most useful to connect queries to the images. However, due to annotation cost, quite limited amount of annotation words are available in many cases. When annotations are not given at all, there needs to be some techniques that assign annotations automatically. When only a few annotation words are given to each image (lightly annotated), there need to be some enhancement techniques that best use the available annotations. We address the later problem by estimating word associations to fill in the lexical gap between queries and annotations. The model of word associations can be learned from the data. However, since images are only lightly annotated, their sparseness in computing word associations becomes crucial. To compensate the sparseness, we propose a novel data exploration technique in which image similarities contribute to the estimation of word associations on the assumption that similar images have similar semantic concepts. We experimentally show the potential benefit of our approach.