数据驱动的大规模图像自动标注关键技术研究

项目名称： 数据驱动的大规模图像自动标注关键技术研究

项目编号： No.61271394

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 丁贵广

作者单位： 清华大学

项目金额： 88万元

中文摘要： 随着网络社区和图像共享网站的发展，网络中产生大量无标签或有很少标签的图像，这给基于关键词的图像检索方法带来了新的挑战。数据驱动的图像自动标注技术是一种有效的图像语义特征提取技术，其利用互联网这个近乎无限的语义仓库和知识仓库，通过数据挖掘、机器学习、计算机视觉等技术自动学习语义概念空间与视觉特征空间的潜在关联或者映射关系，来预测图像的标注。该技术是一个新兴的研究领域，包括众多基础理论和实用技术，其研究具有重要理论意义和广泛应用价值。现有图像自动标注技术尚不能满足大规模网络图像的标注需求，本项目将对数据驱动的图像自动标注技术展开研究，主要研究内容有：网络图像知识库构建与维护、网络图像标签处理、候选标签选择及传播等，并在以上关键技术与算法研究的基础上，研发基于自动标注技术的图像语义检索系统。本项目力争在图像自动标注的理论上有所突破，在技术方法上有所创新，为该项技术的理论研究和实际应用奠定基础。

中文关键词： 图像标注；图像检索；特征索引；；

英文摘要： With the explosive growth of online community and image sharing websites, a huge amount of images with few tags or no tags are being generated on the Web. This tag-incompleteness has posed a great challenge to the keyword-based image retrieval methods and systems. Data-Driven Image Auto-Annotation (DD-IAA) is an effective process for extracting semantic features from images. To automatically predict image tags, DD-IAA learns the latent relationship (mapping) between the semantic concept space and the visual feature space by leveraging the Web as an infinite semantic repository and knowledge base, and utilizing a variety of techniques in data mining, machine learning and computer vision. DD-IAA is a newly-emerged research area. It involves many fundamental theories and practical techniques, which makes its research significant in theory and useful in application. However, the existing methods for image auto-annotation have not yet been matured to support the tagging of large-scale Web images. To tackle this difficulty, in this project, we will make an in-depth study on DD-IAA. Our major research content includes: construction and maintenance of a Web image knowledge base; Web image tag processing; candidate tag selection and propagation, etc. With our own developed key techniques and algorithms, we will implement

英文关键词： Image Annotation；Image Retrieval；Feature Indexing；；