视觉内容动态迁移与关联理解的随机图计算及学习方法研究

项目名称： 视觉内容动态迁移与关联理解的随机图计算及学习方法研究

项目编号： No.61273252

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： 薛建儒

作者单位： 西安交通大学

项目金额： 80万元

中文摘要： 视觉数据的网络化、社会化特征日趋明显，除高维、海量等固有特性外，更与蕴含着相关语义且无序分布的异构数据以不同层次的关联关系共存于网络。现有大多数的视觉语义计算由于孤立地处理单个视觉对象且专注于其固有特性，却对视觉数据的网络异构分布及相互关联等特性重视不够，经常性地陷入求解困难、计算效率低或仅能理解低层语义等困境。本项目聚焦于网络异构视觉数据之间的关联关系，结合类生物视觉皮层的显著性计算模型，研究大规模网络视觉数据的关联推理与语义理解问题。重点研究视觉随机属性图(attribute graph)表征模型与语义动态迁移学习方法，拟提出高维异构视觉数据的关联关系随机属性图、特征降维与关联、特征到高层语义的映射关系等表征模型及语义迁移学习方法，以克服视觉数据语义感知计算中的维数灾难问题，缩短视觉数据理解的"语义鸿沟"。预期的研究成果有助于突破视觉数据语义理解技术瓶颈，发展新型视觉媒体处理技术。

中文关键词： 随机属性图；关联规则学习；语义结构；视觉显著性模型；统计学习

英文摘要： In recent years, network and social characteristics of visual data has become increasingly evident. In addition to its inherent characteristics including high dimension, mass and disordered distribution, the visual data has also become associated with other heterogeneous media data in different levels of relationship due to relevant concepts, themes, and events. Most of the existing visual semantic-aware computing works dealt with a single media object in isolation, and focus only on its inherent characteristics. This often leads them to situations like many difficulties and being low efficiency in computation, and only low level semantics obtained. This is mostly due to their inadequate attention on network and social characteristics of visual data, including heterogeneous distributed and interrelated relationship. This project does research on the problem of semantic understanding of large-scale visual data, via exploiting interrelated association among heterogeneous visual data, as well as integrating with visual cortex-like saliency computing model. Through the research, we try to propose stochastic attribute graph models to represent interrelated relationship, and semantic computing techniques including feature dimension reduction, the mapping from the low-level feature to high-level semantics, and statisti

英文关键词： Stochastic attribute graph；Association rule learning；Semantic structure；Visual saliency modeling；Statistical learning