Efficient Unsupervised Learning for Plankton Images
- URL: http://arxiv.org/abs/2209.06726v1
- Date: Wed, 14 Sep 2022 15:33:16 GMT
- Title: Efficient Unsupervised Learning for Plankton Images
- Authors: Paolo Didier Alfano, Marco Rando, Marco Letizia, Francesca Odone,
Lorenzo Rosasco, Vito Paolo Pastore
- Abstract summary: Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem.
The adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation.
We propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms.
- Score: 12.447149371717
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- MPT: A Large-scale Multi-Phytoplankton Tracking Benchmark [36.37530623015916]
We propose a benchmark dataset, Multiple Phytoplankton Tracking (MPT), which covers diverse background information and variations in motion during observation.
The dataset includes 27 species of phytoplankton and zooplankton, 14 different backgrounds to simulate diverse and complex underwater environments, and a total of 140 videos.
We introduce an additional feature extractor to predict the residuals of the standard feature extractor's output, and compute multi-scale frame-to-frame similarity based on features from different layers of the extractor.
arXiv Detail & Related papers (2024-10-22T04:57:28Z) - Deep-learning-powered data analysis in plankton ecology [31.874825130479174]
The implementation of deep learning algorithms has brought new perspectives to plankton ecology.
Deep learning offers objective schemes to investigate plankton organisms in diverse environments.
arXiv Detail & Related papers (2023-09-15T16:04:11Z) - Towards Generating Large Synthetic Phytoplankton Datasets for Efficient
Monitoring of Harmful Algal Blooms [77.25251419910205]
Harmful algal blooms (HABs) cause significant fish deaths in aquaculture farms.
Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope.
We employ Generative Adversarial Networks (GANs) to generate synthetic images.
arXiv Detail & Related papers (2022-08-03T20:15:55Z) - Ensembles of Vision Transformers as a New Paradigm for Automated
Classification in Ecology [0.0]
We show that ensembles of Data-efficient image Transformers (DeiTs) significantly outperform the previous state of the art (SOTA)
On all the data sets we test, we achieve a new SOTA, with a reduction of the error with respect to the previous SOTA ranging from 18.48% to 87.50%.
arXiv Detail & Related papers (2022-03-03T14:16:22Z) - Deep Learning Classification of Lake Zooplankton [0.0]
We present a set of deep learning models developed for the identification of lake plankton.
To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies.
Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score.
arXiv Detail & Related papers (2021-08-11T14:57:43Z) - Zoo-Tuning: Adaptive Transfer from a Zoo of Models [82.9120546160422]
Zoo-Tuning learns to adaptively transfer the parameters of pretrained models to the target task.
We evaluate our approach on a variety of tasks, including reinforcement learning, image classification, and facial landmark detection.
arXiv Detail & Related papers (2021-06-29T14:09:45Z) - Deep Low-Shot Learning for Biological Image Classification and
Visualization from Limited Training Samples [52.549928980694695]
In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared.
labeling training data with precise stages is very time-consuming even for biologists.
We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
arXiv Detail & Related papers (2020-10-20T06:06:06Z) - A Trainable Optimal Transport Embedding for Feature Aggregation and its
Relationship to Attention [96.77554122595578]
We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference.
Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
arXiv Detail & Related papers (2020-06-22T08:35:58Z) - Two-View Fine-grained Classification of Plant Species [66.75915278733197]
We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species.
A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
arXiv Detail & Related papers (2020-05-18T21:57:47Z) - Automatic image-based identification and biomass estimation of
invertebrates [70.08255822611812]
Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed.
We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology.
We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
arXiv Detail & Related papers (2020-02-05T21:38:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.