A neotropical Miocene pollen database employing image-based search and semantic modeling1

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 efficiency 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 identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

[1]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  A. Traverse.  What Paleopalynology Is and Is Not , 2007 .

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  C. Y. Belsky,et al.  Sporae dispersae der Oberen Kreide von Gabun, Äquatoriales Afrika , 1965 .

[6]  Nil H. Kilicay-Ergin,et al.  Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover , 2013, ISPRS Int. J. Geo Inf..

[7]  J. Muller,et al.  Palynology of tertiary sediments from tropical areas , 1968 .

[8]  A. Lézine,et al.  Pollen databases and their application , 2013 .

[9]  K. Faegri,et al.  Textbook of Pollen Analysis , 1965 .

[10]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[11]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[12]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[13]  González Guzmán.  A palynological study on the upper Los Cuervos and Mirador formations : lower and middle eocene : Tibú area, Colombia , 1967 .

[14]  Luis Ibáñez,et al.  The ITK Software Guide , 2005 .

[15]  S. Punyasena,et al.  On the Taxonomic Resolution of Pollen and Spore Records of Earth’s Vegetation , 2014, International Journal of Plant Sciences.

[16]  M. Bebbington,et al.  Separating Morphologically Similar Pollen Types Using Basic Shape Features from Digital Images: A Preliminary Study , 2014 .

[17]  Luke Mander,et al.  Accuracy and consistency of grass pollen identification by human analysts using electron micrographs of surface ornamentation1 , 2014, Applications in plant sciences.

[18]  Gustavo Adolfo Sarmiento Pérez.  Palinología de la formación Guaduas. Estratigráfica y sistemática , 1992 .

[19]  C. Jaramillo,et al.  Neogene palynology of the Solimões Basin, Brazilian Amazonia , 2010 .

[20]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[21]  Alex Zelinsky,et al.  Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf] , 2009, IEEE Robotics & Automation Magazine.

[22]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[23]  P. V. Hoeken-Klinkenberg.  A palynological investigation of some Upper-Cretaceous sediments in Nigeria. Pollen and Spores , 1964 .

[24]  Chi-Ren Shyu,et al.  Knowledge-Driven Multidimensional Indexing Structure for Biomedical Media Database Retrieval , 2007, IEEE Transactions on Information Technology in Biomedicine.

[25]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[26]  K. Holt,et al.  Principles and methods for automated palynology. , 2014, The New phytologist.

[27]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[28]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[29]  T. V. D. Hammen.  Description of some genera and species of fossil pollen and spores , 1956 .

[30]  D. Tcheng,et al.  Classifying black and white spruce pollen using layered machine learning. , 2012, The New phytologist.

[31]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  A. Carlini,et al.  Urumaco and Venezuelan Paleontology: The Fossil Record of the Northern Neotropics , 2010 .

[33]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[34]  Stephen Blackmore,et al.  Glossary of pollen and spore terminology , 2007 .

[35]  T. V. D. Hammen,et al.  The Paleocene pollen flora of Colombia , 1965 .

[36]  H. Birks,et al.  Identification of Picea pollen of Late Quaternary age in eastern North America: a numerical approach , 1980 .

[37]  Kitae Song,et al.  Applications in Plant Sciences , 2022 .

[38]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[39]  D. Dilcher,et al.  Middle Paleogene palynology of Central Colombia, South America: A study of pollen and spores from tropical latitudes , 2001 .

[40]  Jack Sklansky,et al.  Finding the convex hull of a simple polygon , 1982, Pattern Recognit. Lett..

[41]  Chi-Ren Shyu,et al.  Improving retrieval performance in medical image databases using simulated annealing. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[42]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[43]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[44]  R. M. Hodgson,et al.  Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory , 2011 .

[45]  Mark B. Bush,et al.  Introducing a new (freeware) tool for palynology , 2007 .

[46]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[47]  H. Dueñas.  Palynology of oligocene—Miocene strata of borehole Q-E-22, Planeta Rica, Northern Colombia , 1980 .

[48]  T. Hammen,et al.  A palynological study on the Tertiary and Upper Cretaceous of British Guiana , 1964 .

[49]  Heber MacMahon,et al.  Automated lung segmentation and computer-aided diagnosis for thoracic CT scans , 2003, CARS.

[50]  Chi-Ren Shyu,et al.  Visual-Semantic Modeling in Content-Based Geospatial Information Retrieval Using Associative Mining Techniques , 2010, IEEE Geoscience and Remote Sensing Letters.

[51]  C. Hoorn,et al.  An environmental reconstruction of the palaeo-Amazon river system (Middle to late Miocene, N.W. Amazonia , 1994 .