Learning Concepts in Morphological Image Databases

学习形态图像数据库中的概念

• 批准号: 0641076
• 负责人: Bir Bhanu
• 金额: $ 74.27万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0641076&HistoricalAwards=false
• 关键词: Learning; Concepts; Morphological; Image; Databases

The University of California-Riverside is awarded a grant to develop a formal framework for morphological image databases, which automatically extracts appropriate visual features from images, allows user feedback, exploits the accumulated meta knowledge, effectively learns visual concepts for different insect species, efficiently manipulates database entities, and significantly improves the image retrieval performance. The techniques will be validated by performing scientific experiments on multiple databasesusing a variety of quantitative performance evaluation measures. The project involves an interdisciplinary team and a close collaboration between biologists and computer scientists and interactions with severalother biologists from the University of Pennsylvania and the University of California at Riverside who are interested in this effort and who work with insects. The project develops novel techniques for the extraction of morphological information including: (1) use of local/global features at multiple levels of abstraction in a sound mathematical framework of semi-supervised learning; (2) use of relevance feedback and long-termlearning from multiple users; (3) analysis of similarity based on local patch-based representation and relations between the patches which will provide geometric morphometrics and ultimately will lead to theunderstanding of character; and (4) novel spatial indexing structure that account for the uncertainty and can handle large amounts of data. Morphological features recognized by the system would be an aid to rapid, automated species identification and search in image and video databases. A prototype of the system will be coupled to MorphNet, an existing image database server at UCR.

加州河畔大学获得了赠款，以开发形态图像数据库的正式框架，该框架自动从图像中提取适当的视觉特征，允许用户反馈，利用累积的元知识，有效地学习不同昆虫物种的视觉概念，有效地操纵数据库实体，并显着改善图像重试性能。这些技术将通过在多个数据库上进行多种定量性能评估指标进行科学实验来验证。该项目涉及一个跨学科团队，以及生物学家与计算机科学家之间的密切合作，以及与宾夕法尼亚大学和加利福尼亚大学里弗赛尔大学的几位生物学家的互动，他们对这项工作以及与昆虫一起工作感兴趣。 该项目开发了用于提取形态学信息的新技术，包括：（1）在半监督学习的合理数学框架中使用多个抽象的局部/全球特征； （2）使用相关性反馈和多个用户的长期学习； （3）基于基于局部斑块的表示和斑块之间的关系分析，这些贴片将提供几何形态计量学，并最终导致特征的理解； （4）解释不确定性并可以处理大量数据的新型空间索引结构。该系统认可的形态特征将有助于在图像和视频数据库中快速，自动化物种识别和搜索。系统的原型将与UCR上现有的图像数据库服务器耦合到Morphnet。