A4 Conference proceedings

Plankton Recognition in Images with Varying Size


Open Access publication

Publication Details
Authors: Bureš Jaroslav, Eerola Tuomas, Lensu Lasse, Kälviäinen Heikki, Zemčík Pavel
Publisher: Springer Verlag (Germany): Series
Publication year: 2021
Language: English
Related journal or series: Lecture Notes in Computer Science
Title of parent publication: Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12666.
Journal name in source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal acronym: LNCS
Volume number: 12666
Start page: 110
End page: 120
Number of pages: 11
ISBN: 978-3-030-68779-3
eISBN: 978-3-030-68780-9
ISSN: 0302-9743
eISSN: 1611-3349
JUFO level of this publication: 1
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2021050629047

Abstract

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.


KeywordsConvolutional neural networks, Plankton recognition, Varying input size

Last updated on 2021-06-05 at 13:55