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Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains.

ABSTRACT: The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper.

SUBMITTER: Goncalves AB

PROVIDER: S-EPMC4898734 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains.

Gonçalves Ariadne Barbosa AB Souza Junior Silva JS Silva Gercina Gonçalves da GG Cereda Marney Pascoli MP Pott Arnildo A Naka Marco Hiroshi MH Pistori Hemerson H

PloS one 20160608 6

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer ...[more]

PMID: 27276196

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