First Vector Graphic

Main Contact : Alessandro Ortis ortis@dmi.unict.it


App
Challenge Description

The aim of the proposed challenge is the automatic classification of pollen grain images exploiting the largest dataset of microscope pollen grain images, collected from aerobiological samples. The microscope images of the samples have been digitalized and processed through a proper image processing pipeline to detect and extract four classes of objects, including three species of pollen grain and an additional class of objects that could be often mis-classified as pollen (e.g., air bubbles, dust, etc.). More than 13.000 objects have been detected and labelled by aerobiology experts.

Evaluation Criteria

Participants are requested to upload the results of classification according to the submission format that will be reported on the challenge website.

The competition website will provide additional information about the data, material and code examples that perform a preliminary data exploration.

Test classification results will be evaluated using the weighted F1 score for quantitative evaluation. This metric has been selected considering the imbalance in the data. This function calculates the F1 metrics for each class, and their average weighted by support (the number of true instances for each class). This alters the unweighted F1 score to account for label imbalance.

App
Participants will present their works during a competition track at ICPR 2020, co-located with the conference, where competition results will be presented and discussed. In addition, the authors of the best selected works will be invited to contribute to a joint paper on the addressed challenge. The paper will include the most significative works and their findings, will be submitted to a valuable Journal.

Dataset

The provided dataset consists of more than 13 thousands per-object images collected from aerobiological samples, classified into four different categories:

  • - Corylus avellana, well-developed pollen grains
  • - Corylus avellana, anomalous pollen grains
  • - Alnus, well-developed pollen grains
  • - Debris (bubbles, dust and any non-pollen detected object)

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Figure 1 – Example of class 1 (Normal Pollen)

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Figure 2 – Example of class 2 (Anomalous Pollen)

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Figure 3 – Example of class 3 (Alnus)

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Figure 4 – Example of class 4 (Debris)


Important Dates
  • 10/04/2020

    Registration opening


  • 20/04/2020

    Training data available:


  • 18/05/2020

    Testing data available:


  • 22/05/2020

    Test result submission deadline


  • 30/06/2020

    Announcement of the evaluation results: