International Conference on Pattern Recognition是IAPR的旗舰会议、国际模式识别协会和模式识别领域的首场会议,包括计算机视觉、图像、声音、语音、传感器模式处理和机器智能。ICPR2020是这一系列的第25个项目,从开始到现在已经50岁了。ICPR 2020将是一个为期6天的活动,包括研讨会、辅导、主要会议、研究成果展示、科学竞赛和展览。它将汇集世界范围内该领域的顶尖研究人员,并为与会者提供互动和培养新思想和合作的机会。官网链接:https://www.micc.unifi.it/icpr2020/

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Pollen grain micrograph classification has multiple applications in medicine and biology. Automatic pollen grain image classification can alleviate the problems of manual categorisation such as subjectivity and time constraints. While a number of computer-based methods have been introduced in the literature to perform this task, classification performance needs to be improved for these methods to be useful in practice. In this paper, we present an ensemble approach for pollen grain microscopic image classification into four categories: Corylus Avellana well-developed pollen grain, Corylus Avellana anomalous pollen grain, Alnus well-developed pollen grain, and non-pollen (debris) instances. In our approach, we develop a classification strategy that is based on fusion of four state-of-the-art fine-tuned convolutional neural networks, namely EfficientNetB0, EfficientNetB1, EfficientNetB2 and SeResNeXt-50 deep models. These models are trained with images of three fixed sizes (224x224, 240x240, and 260x260 pixels) and their prediction probability vectors are then fused in an ensemble method to form a final classification vector for a given pollen grain image. Our proposed method is shown to yield excellent classification performance, obtaining an accuracy of of 94.48% and a weighted F1-score of 94.54% on the ICPR 2020 Pollen Grain Classification Challenge training dataset based on five-fold cross-validation. Evaluated on the test set of the challenge, our approach achieved a very competitive performance in comparison to the top ranked approaches with an accuracy and a weighted F1-score of 96.28% and 96.30%, respectively.

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