Planktonic organisms are key components of aquatic ecosystems and respond
quickly to changes in the environment, therefore their monitoring is vital to
understand the changes in the environment. Yet, monitoring plankton at
appropriate scales still remains a challenge, limiting our understanding of
functioning of aquatic systems and their response to changes. Modern plankton
imaging instruments can be utilized to sample at high frequencies, enabling
novel possibilities to study plankton populations. However, manual analysis of
the data is costly, time consuming and expert based, making such approach
unsuitable for large-scale application and urging for automatic solutions. The
key problem related to the utilization of plankton datasets through image
analysis is plankton recognition. Despite the large amount of research done,
automatic methods have not been widely adopted for operational use. In this
paper, a comprehensive survey on existing solutions for automatic plankton
recognition is presented. First, we identify the most notable challenges that
that make the development of plankton recognition systems difficult. Then, we
provide a detailed description of solutions for these challenges proposed in
plankton recognition literature. Finally, we propose a workflow to identify the
specific challenges in new datasets and the recommended approaches to address
them. For many of the challenges, applicable solutions exist. However,
important challenges remain unsolved: 1) the domain shift between the datasets
hindering the development of a general plankton recognition system that would
work across different imaging instruments, 2) the difficulty to identify and
process the images of previously unseen classes, and 3) the uncertainty in
expert annotations that affects the training of the machine learning models for
recognition. These challenges should be addressed in the future research.

通过综述现有方案，本文讨论了浮游生物自动识别中最大的挑战，并提出新数据集中面临的难点以及解决方案，并指出仍存在的几大问题，如不同数据集之间的领域转移、未曾见过的类别的图像处理难度和专家注释的不确定性，这需要加以解决。

自动浮游生物图像识别调查：挑战、现有解决方案和未来展望

Survey of Automatic Plankton Image Recognition: Challenges, Existing  Solutions and Future Perspectives

Monitoring plankton populations in situ is fundamental to preserve the
aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor
environmental perturbations, that can reflect into consequent morphological and
dynamical modifications. Nowadays, the availability of advanced automatic or
semi-automatic acquisition systems has been allowing the production of an
increasingly large amount of plankton image data. The adoption of machine
learning algorithms to classify such data may be affected by the significant
cost of manual annotation, due to both the huge quantity of acquired data and
the numerosity of plankton species. To address these challenges, we propose an
efficient unsupervised learning pipeline to provide accurate classification of
plankton microorganisms. We build a set of image descriptors exploiting a
two-step procedure. First, a Variational Autoencoder (VAE) is trained on
features extracted by a pre-trained neural network. We then use the learnt
latent space as image descriptor for clustering. We compare our method with
state-of-the-art unsupervised approaches, where a set of pre-defined
hand-crafted features is used for clustering of plankton images. The proposed
pipeline outperforms the benchmark algorithms for all the plankton datasets
included in our analysis, providing better image embedding properties.

本文提出了一种高效的无监督学习流程来提供准确的浮游生物分类，并比较其与现有技术，在所有数据集中性能更好，提供更好的图像嵌入特性。

浮游生物图像的高效无监督学习

Efficient Unsupervised Learning for Plankton Images

Planktonic organisms are of fundamental importance to marine ecosystems: they
form the basis of the food web, provide the link between the atmosphere and the
deep ocean, and influence global-scale biogeochemical cycles. Scientists are
increasingly using imaging-based technologies to study these creatures in their
natural habit. Images from such systems provide an unique opportunity to model
and understand plankton ecosystems, but the collected datasets can be enormous.
The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for
example, is an \emph{in situ} system that has been continuously imaging
plankton since 2006. To date, it has generated more than 700 million samples.
Manual classification of such a vast image collection is impractical due to the
size of the data set. In addition, the annotation task is challenging due to
the large space of relevant classes, intra-class variability, and inter-class
similarity. Methods for automated classification exist, but the accuracy is
often below that of human experts. Here we introduce WHOI-Plankton: a large
scale, fine-grained visual recognition dataset for plankton classification,
which comprises over 3.4 million expert-labeled images across 70 classes. The
labeled image set is complied from over 8 years of near continuous data
collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO).
We discuss relevant metrics for evaluation of classification performance and
provide results for a traditional method based on hand-engineered features and
two methods based on convolutional neural networks.

科学家们越来越多地使用基于成像的技术研究浮游生物，而 WHOI-Plankton 是一个大规模、细粒度的可视化识别数据集，包括了 70 类超过 340 万张的专家标记图像，用于通过传统方法和两种基于卷积神经网络的方法来进行自动分类，并讨论了相关的分类性能评估指标和结果。