Publication Details
Plankton Recognition in Images with Varying Size
Eerola Tuomas, doc., Ph.D. (LUT)
Lensu Lasse (TUT)
Kälviäinen Heikki, prof. (LUT)
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT)
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{FITPUB12480,
author = "Jaroslav Bure\v{s} and Tuomas Eerola and Lasse Lensu and Heikki K{\"{a}}lvi{\"{a}}inen and Pavel Zem\v{c}\'{i}k",
title = "Plankton Recognition in Images with Varying Size",
pages = "110--120",
journal = "Lecture Notes in Computer Science",
volume = 12666,
number = 2,
year = 2021,
ISSN = "0302-9743",
doi = "10.1007/978-3-030-68780-9\_11",
language = "english",
url = "https://www.fit.vut.cz/research/publication/12480"
}