基于上下文感知的互联网社群图像语义理解

项目名称： 基于上下文感知的互联网社群图像语义理解

项目编号： No.61272352

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： 郎丛妍

作者单位： 北京交通大学

项目金额： 80万元

中文摘要： 图像语义理解是近年来计算机视觉中一个非常活跃的研究方向。本课题的研究目标是面向互联网社群图像的自动语义理解。主要研究内容:(1)基于视觉注意力机制的图像显著性分析和表示；(2)针对互联网图像级标签的特点，以多边图的图像描述方法为基础，研究图像级标签向区域级标签的传播算法，实现高效的图像区域级标注；(3)挖掘图像语义层间的上下文信息提取与表示，研究自适应的上下文信息融合学习算法，并应用到图像多级语义的协同优化问题中；(4)社群图像包含丰富且存在大量带噪声的标签信息，研究自动图像标签精准化问题。本项目的特色是：(1)基于认知理论的图像表示为图像理解提供有效的表示单元；同时为图像区域级标注提供区域语义重要度信息。（2）以互联网社群图像的视觉特征和标签信息为研究对象，充分挖掘图像语义的上下文信息，提出有效的互联网图像理解框架与模式，并提出新的异构媒体计算方法，为大规模互联网图像提供有效的检索途径。

中文关键词： 上下文语义信息；图像语义内容分析；显著性分析；社群图像标注；多示例半监督学习

英文摘要： Automatic image annotation has emerged as an important research topic. The aim of our research topic is to annotate semantic keywords automatically for the social images. Online social media services such as Flickr and Zooomr allow users to share their images with the others for social interaction. In order to understand these social images, the main contributions of the proposed research topic are as follows: (1) In order to make computer vision system have the similar visual perception function, we focus on the image representation based on visual attention model. Specially, the saliency model based on low-rank matrix decompostion will be stuied .(2) We aim to propose a multi-edge graph to model the multiple relationships among the semantic regions of two images. By propagating tag information over the graph structure, we naturally achieve the tag-to-region assignment, leading to more fine tag information while improving the reliability of content-based image retrieval. (3) We investigate how to iteratively and mutually boost image-level and region-level annotation by taking the outputs from one task as the context of the other one. Instead of intuitive feature and context concatenation or post-processing with context, we focus on the context-adaptive classifier which takes the responsibility of dynamically a

英文关键词： Context Information；Image Semantic Analysis；Visual Attention；Social Image Tagging；muliti Instance Semi-supervised Learning