Publication Details
Plankton Recognition in Images with Varying Size
Eerola Tuomas, Prof.
Lensu Lasse
Kälviäinen Heikki
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
plankton monitoring, mechine learning with varying size images, convlutional
neural networks CNN
Monitoring plankton is important as they are an essential part of the aquatic
food web as well as producers of oxygen. Modern imaging devices produce a massive
amount of plankton image data which calls for automatic solutions. These images
are characterized by a very large variation in both the size and the aspect
ratio. Convolutional neural network (CNN) based classification methods, on the
other hand, typically require a fixed size input. Simple scaling of the images
into a common size contains several drawbacks. First, the information about the
size of the plankton is lost. For human experts, the size information is one of
the most important cues for identifying the species. Second, downscaling the
images leads to the loss of fine details such as flagella essential for species
recognition. Third, upscaling the images increases the size of the network. In
this work, extensive experiments on various approaches to address the varying
image dimensions are carried out on a challenging phytoplankton image dataset.
A novel combination of methods is proposed, showing improvement over the baseline
CNN.
@article{BUT187364,
author="Jaroslav {Bureš} and Tuomas {Eerola} and Lasse {Lensu} and Heikki {Kälviäinen} and Pavel {Zemčík}",
title="Plankton Recognition in Images with Varying Size",
journal="Lecture Notes in Computer Science",
year="2021",
volume="12666",
number="2",
pages="110--120",
doi="10.1007/978-3-030-68780-9\{_}11",
issn="0302-9743",
url="https://link.springer.com/chapter/10.1007%2F978-3-030-68780-9_11"
}