# Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification

arXiv: https://arxiv.org/abs/1605.00775 · HTML: https://mlsanity.com/paper/1605.00775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2016.

## Built on by (most cited first, 1 of 1)

- [Scalable Detection of Fossil Palynomorphs in Multifocal Digital Microscopy Images](https://mlsanity.com/paper/2609.05323/) (2026)

## Abstract (arXiv)

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.