Detection and Classification of Pollen Grain Microscope Images

Battiato, Sebastiano; Ortis, Alessandro; Trenta, Francesca; Ascari, Lorenzo; Politi, Mara; Siniscalco, Consolata

doi:10.1109/cvprw50498.2020.00498

Cited by 29 publications

(23 citation statements)

References 13 publications

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“…If the pollen taxa get reduced to, e.g., the number of allergy relevant pollen, a worldwide data set could be established and be helpful not just for automatic pollen classification. The recent data sets POLLEN13K [4] and the New Zealand pollen set [60] are steps in the right direction. Especially the latter, due to its large number of pollen taxa, could be used as a benchmark data set.…”

Section: Data Setsmentioning

confidence: 99%

Pattern recognition methodologies for pollen grain image classification: a survey

Viertel

König

2022

Machine Vision and Applications

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In a large number of scientific areas, such as immunology, forensics, paleoecology, and archeology, the study of pollen, i.e., palynology, plays an important role: from tracking climate changes, studying allergies, to forensic investigations or honey origin analysis. Since the mid-nineties of the last century, the idea for an automated solution to the problem of pollen identification and classification was formulated and since then, several attempts and proposals have been made and presented, based on different technologies, in particular in the field of Computer Vision. However, as of 2021 microscopic analyses are performed mainly manually by highly trained specialists, although the capabilities of artificial intelligence, especially Deep Neural Networks, are steadily increasing. In this work, we analyzed various state-of-the-art research work concerning pollen detection and classification and compared their methods and results. The problems, such as data accessibility, different methods of Machine Learning, and the intended applicability of the proposed solutions are explored. We also identified crucial issues that require further work and research. Our work will provide a thorough view on the current state of the art, its issues, and possibilities for the future.

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Section: Data Setsmentioning

confidence: 99%

Pattern recognition methodologies for pollen grain image classification: a survey

Viertel

König

2022

Machine Vision and Applications

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“…For these experiments, we set the same parameters used for AlexNet. SmallerVGGNet achieved an average F1 score of 0.85 using augmented dataset and an average score of 0.69 using images with noisy background [24].…”

Section: Experiments Using Deep Learning Modelsmentioning

confidence: 99%

POLLEN13K: A Large Scale Microscope Pollen Grain Image Dataset

Battiato

Ortis

Trenta

et al. 2020

2020 IEEE International Conference on Image Processing (ICIP)

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Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented.

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“…The task of detection of pollen and the type of the pollen is one of the tasks in field of aero-biology. Ability to perform such tasks can help with the detection of allergens and other infectious diseases [2]. Deep learning methods have shows great success in achieving state of the art results on many computer vision tasks including image recognition [10], [7], [11] etc..…”

Section: Introductionmentioning

confidence: 99%

Robust Pollen Imagery Classification with Generative Modeling and Mixup Training

Murkute

2021

Preprint

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Deep learning based approaches have shown great success in image classification tasks and can aid greatly towards the fast and reliable classification of pollen grain aerial imagery. However, often-times deep learning methods in the setting of natural images can suffer generalization problems and yields poor performance on unseen test distribution. In this work, we present and a robust deep learning framework that can generalize well for pollen grain aerobiological imagery classification.We develop a convolutional neural network based pollen grain classification approach and combine some of the best practices in deep learning for better generalization. In addition to commonplace apparoches like data-augmentation and weight regularization we utilize implicit regularization methods like manifold mixup to allow learning of smoother decision boundaries. We also make use of proven state-of-the-art architectural choices like EfficientNet convolutional neural networks. Inspired by the success of generative modeling with variational autoencoders, we train models with a richer learning objective which can allow model to focus on the relevant parts of the image. Finally, we create an ensemble of neural networks, for robustness of the test set predictions. Based on our experiments, we show improved generalization performance as measured with weighted F1-score with aforementioned approaches. The proposed approach earned a fourth-place in the final rankings in the ICPR-2020 Pollen Grain Classification Challenge; with a 0.972578 weighted F1 score, 0.950828 macro average F1 score and 0.972877 recognition accuracy.

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Detection and Classification of Pollen Grain Microscope Images

Cited by 29 publications

References 13 publications

Pattern recognition methodologies for pollen grain image classification: a survey

Pattern recognition methodologies for pollen grain image classification: a survey

POLLEN13K: A Large Scale Microscope Pollen Grain Image Dataset

Robust Pollen Imagery Classification with Generative Modeling and Mixup Training

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