Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology. We introduce a novel criteria for selecting meaningful and discriminative exemplar patches. We optimize this function during training using a greedy submodular function optimization framework that gives a near-optimal solution with bounded approximation error. We use these selected exemplars as a dictionary basis and propose a spatially-aware sparse coding method to match testing images for identification while maintaining global shape correspondence. To accelerate the coding process for fast matching, we introduce a relaxed form that uses spatially-aware soft-thresholding during coding. Finally, we carry out an experimental study that demonstrates the effectiveness and efficiency of our exemplar selection and classification mechanisms, achieving 86.13% accuracy on a difficult fine-grained species classification task distinguishing three types of fossil spruce pollen.
Text ReferenceShu Kong, Surangi Punyasena, and Charless Fowlkes. Spatially aware dictionary learning and coding for fossil pollen identification. In CVPR workshop CVMI. 2016.
AUTHOR = "Kong, Shu and Punyasena, Surangi and Fowlkes, Charless",
TITLE = "Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification",
BOOKTITLE = "CVPR workshop CVMI",
YEAR = "2016",
tag = "grouping,object_recognision,biological_images"