Learning Concepts in Morphological Image Databases

加州大学河滨分校获得资助，开发了一个形式化的图像数据库框架，该框架自动从图像中提取适当的视觉特征，允许用户反馈，利用积累的元知识，有效地学习不同昆虫物种的视觉概念，高效地操作数据库实体，并显著提高图像检索性能。这些技术将通过在多个数据库上使用各种定量业绩评估措施进行科学实验来验证。该项目涉及一个跨学科的团队，生物学家和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州大学河滨分校的其他几位生物学家的互动，这些生物学家对这项工作感兴趣，并与昆虫有关。该项目开发了用于提取形态信息的新技术，包括：(1)在健全的半监督学习的数学框架中使用局部/全局特征；(2)使用相关反馈和来自多用户的长期学习；(3)基于局部基于块的表示和块之间的关系的相似性分析，这将提供几何形态测量并最终导致对字符的理解；以及(4)考虑到不确定性并能够处理大量数据的新颖的空间索引结构。该系统识别的形态特征将有助于在图像和视频数据库中快速、自动化地识别和搜索物种。该系统的原型将与UCR现有的图像数据库服务器MorphNet相连。

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.