SGER: Exploratory Investigation of Hierarchical Image Classification and Database Indexing

SGER：分层图像分类和数据库索引的探索性研究

• 批准号: 0601542
• 负责人: Jianping Fan
• 金额: --
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0601542&HistoricalAwards=false
• 关键词: SGER; Exploratory; Investigation; Hierarchical; Image

Title: SGER: Exploratory Investigation of Hierarchical Image Classification and Database IndexingPI: Jianping FanAbstractAs high-resolution digital cameras become more affordable and widespread, high-quality digital images become ever more available and useful. There is an urgent need to support more effective image retrieval over large-scale image archives. In this Small Grants for Experimental Research (SGER) project, a novel framework for image content representation will be developed based on using salient objects to characterize middle-level image semantics. For salient object detection, automatic image segmentation will be integrated with image region classification. Also, hierarchical image classification will be incorporated to enable concept-oriented image database indexing and retrieval through exploring the underlying relationships among different semantic image concepts. Hierarchical finite mixture models will be presented for multi-level semantic image concept modeling, image classification and database indexing, and an adaptive Expectation Maximization (EM) algorithm will be developed for model selection and parameter estimation. Finally, a novel framework for statistical image modeling will be developed to enable partial image matching in the procedures for classification and retrieval. The research will lead to useful tools that could significantly impact computer vision, image database management, digital library or museum management, and image data mining for a wide range of applications. The project will provide a good environment for interdisciplinary education in information technology that bridges the gap between traditional areas of computer vision, databases, visual information retrieval, and machine learning to benefit students of all levels.Project web page: http://www.cs.uncc.edu/~jfan/project3.html

题目：SGER：层次图像分类与数据库索引的探索性研究【摘要】随着高分辨率数码相机的价格和普及，高质量的数字图像变得越来越容易获得和有用。迫切需要支持大规模图像档案中更有效的图像检索。在这个实验研究小额拨款（SGER）项目中，将基于使用显著对象来表征中级图像语义，开发一个新的图像内容表示框架。对于显著目标检测，将自动图像分割与图像区域分类相结合。此外，分层图像分类将被纳入，通过探索不同语义图像概念之间的潜在关系，实现面向概念的图像数据库索引和检索。将提出用于多级语义图像概念建模、图像分类和数据库索引的分层有限混合模型，并开发用于模型选择和参数估计的自适应期望最大化（EM）算法。最后，将开发一种新的统计图像建模框架，使分类和检索过程中的部分图像匹配成为可能。这项研究将导致有用的工具，可以显著影响计算机视觉，图像数据库管理，数字图书馆或博物馆管理，以及广泛应用的图像数据挖掘。该计划将为资讯科技的跨学科教育提供良好的环境，弥补传统的计算机视觉、数据库、视觉资讯检索和机器学习领域之间的差距，使各级学生受益。项目网页：http://www.cs.uncc.edu/~jfan/project3.html