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Tackling inter-class similarity and intra-class variance for microscopic image-based classification

Abstract : Automatic classification of aquatic microorganisms is based on the morphological features extracted from individual images. The current works on their classification do not consider the inter-class similarity and intra-class variance that causes misclassification. We are particularly interested in the case where variance within a class occurs due to discrete visual changes in microscopic images. In this paper, we propose to account for it by partitioning the classes with high variance based on the visual features. Our algorithm automatically decides the optimal number of sub-classes to be created and consider each of them as a separate class for training. This way, the network learns finer-grained visual features. Our experiments on two databases of freshwater benthic diatoms and marine plankton show that our method can outperform the state-of-the-art approaches for classification of these aquatic microorganisms.
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Contributor : Martin Laviale Connect in order to contact the contributor
Submitted on : Sunday, September 12, 2021 - 2:03:05 PM
Last modification on : Tuesday, May 10, 2022 - 3:38:05 PM

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Aishwarya Venkataramanan, Martin Laviale, Cécile Figus, Philippe Usseglio-Polatera, Cedric Pradalier. Tackling inter-class similarity and intra-class variance for microscopic image-based classification. 13th International Conference on Computer Vision Systems (ICVS 2021), Sep 2021, Virtual Event, Austria. ⟨10.1007/978-3-030-87156-7_8⟩. ⟨hal-03341742⟩



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