2016/05/03 by Shu Kong, Surangi Punyasena, Kong, Shu +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · Psychology · #Allergic Rhinitis and Sensitization #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Insect and Arachnid Ecology and Behavior #Populations and Evolution (q-bio.PE) #Primate Behavior and Ecology #Quantitative Methods (q-bio.QM) #cs.CV #q-bio.PE #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1605.00775
CVMI 2016
arxiv created 2016/05/03 · openalex publication_date 2016/05/03 · arxiv updated 2016/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.