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 databases using 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 several other 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-term learning 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 the understanding 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.
