基于社会标签的图像标注与标签推荐

项目名称： 基于社会标签的图像标注与标签推荐

项目编号： No.61272329

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 刘静

作者单位： 中国科学院自动化研究所

项目金额： 80万元

中文摘要： 基于社会标签在网络图片资源传播分享中的重要作用，同时考虑到因其自由性和开放性而带来的多噪声、稀疏性、主观性等问题，本项目将以图片分享网站的社会标签、图像与用户数据为研究对象，合理考虑三者之间动态交互关系，重点发掘社会标签数据在语义空间学习、图像特征表示、图像标注及其标签推荐等方面所能提供的有用知识，具体展开以下研究内容：（1）社会标签的层次关系发现及其相关性分析；（2）融合高层语义的图像特征表示及其相关性分析；（3）基于矩阵分解模型的图像-标签关联分析（即图像标注）；（4）基于用户偏好学习的图像标签推荐。其中，图像标签推荐可降低用户标注代价，提高标注数据质量与数量，进而为图像标注提供更好的数据基础；反过来，图像标注可为用户要标记的图像提供其语义标签的先验排序，以此为标签推荐提供指导。二者的有机结合是维持网络共享资源的增量式高效管理与准确索引的有力保证。

中文关键词： 图像标注；标签推荐；社会标签；子空间学习；

英文摘要： With the permeation of Web 2.0, large-scale user contributed images with tags are easily available on social websites. Due to the subjectivity and diversity of such social tagging, noisy and missing tags for images are inevitable, which limits the performance of tag-based image retrieval system. In this proposal, we aim to solve the problems of image annotation and tag recommendation by exploring the correlations among images, tags, and users, in which the usage of the social tags should be taken more attention. We plan to carry out the project from the following aspects: (1) hierarchical structure learning and correlation estimation for social tags; (2) semantic-incorporated image representation and similarity measure; (3) improved matrix factorization for image annotation; (4) image tag recommendation based on user preference learning. It is noted that image annotation and tag recommendation are boosted each other. Due to the decreased tagging cost brought by tag recommendation, more users are willing to tag images, then more high-quality tagging data can be obtained for image annotation methods. In addition, the results of image annotation can provide prior guidance for image tag recommendation. We believe that the union of image annotation and tag recommendation make it possible to indexing and search the la

英文关键词： Image Annotation；Tag Recommendation；Social Tag；Sub-space Learning；