A neotropical miocene pollen database employing image-based search and semantic modeling

Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and effi ciency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. Discussion : Content-And semantic-based image searches provide a powerful computational platform for pollen and spore identifi cation. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identifi cation, analysis, and species discovery. .