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1. 发明授权

US08571850B2 Dual cross-media relevance model for image annotation 有权
标题翻译：用于图像注释的双跨媒体相关性模型
公开(公告)号：US08571850B2
公开(公告)日：2013-10-29
申请号：US11956331
申请日：2007-12-13
申请人： Mingjing Li , Jing Lui , Bin Wang , Zhiwei Li , Wei-Ying Ma
发明人： Mingjing Li , Jing Lui , Bin Wang , Zhiwei Li , Wei-Ying Ma
IPC分类号： G06F17/27
CPC分类号： G06F17/241 , G06F17/2735
摘要： A dual cross-media relevance model (DCMRM) is used for automatic image annotation. In contrast to the traditional relevance models which calculate the joint probability of words and images over a training image database, the DCMRM model estimates the joint probability by calculating the expectation over words in a predefined lexicon. The DCMRM model may be advantageous because a predefined lexicon potentially has better behavior than a training image database. The DCMRM model also takes advantage of content-based techniques and image search techniques to define the word-to-image and word-to-word relations involved in image annotation. Both relations can be estimated by using image search techniques on the web data as well as available training data.
摘要翻译：双重跨媒体相关性模型（DCMRM）用于自动图像注释。与在训练图像数据库中计算单词和图像的联合概率的传统相关性模型相反，DCMRM模型通过计算预定义词典中的单词的期望来估计联合概率。 DCMRM模型可能是有利的，因为预定义词典潜在地具有比训练图像数据库更好的行为。 DCMRM模型还利用基于内容的技术和图像搜索技术来定义图像注释中涉及的单词到图像和单词对字的关系。可以通过使用图像搜索技术对网络数据以及可用的训练数据来估计这两个关系。

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