SGER: Developing a Non-parametric Digital Image Search Engine

SGER：开发非参数数字图像搜索引擎

• 批准号: 9707090
• 负责人: Ari Gross
• 金额: $ 5万
• 依托单位: CUNY Queens College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-02-01 至 1998-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9707090&HistoricalAwards=false
• 关键词: SGER; Developing; Non; parametric; Digital

*** This award, in the Small Grants for Exploratory Research mode, will investigate ways to group images to facilitate the development of effective indexing methods for image search. Most current approaches to this problem focus on texture and color characteristics in the images. This effort, building on prior funded research in this laboratory on nonparametric shape modeling, seeks to determine whether structural encoding can be used more effectively to give the search engine more information about the image, and thereby facilitate the search. Generally, contemporary approaches have avoided relying on such structural information as too difficult to obtain. The planned approach will combine geometrical and topological considerations such as symmetry, parallelness, orthogonality, and connectivity to gainmore of an understanding of the image content, leading to the production of digital geometric primitives to aid in image search. These geometric/topologic primitives should complement the texture and color information currently used for this purpose.***

*这个奖项，在探索性研究小额拨款模式下，将调查如何对图像进行分组，以促进开发有效的图像搜索索引方法。目前解决这一问题的方法大多集中在图像的纹理和颜色特征上。这项工作建立在该实验室先前资助的非参数形状建模研究的基础上，旨在确定是否可以更有效地使用结构编码来为搜索引擎提供更多关于图像的信息，从而促进搜索。一般来说，当代的方法避免依赖于太难获得的结构性信息。计划中的方法将结合几何和拓扑考虑因素，如对称性、平行度、正交性和连通性，以获得对图像内容的更多理解，从而产生数字几何基元来辅助图像搜索。这些几何/拓扑基元应补充当前用于此目的的纹理和颜色信息。*