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Cross-validation of a semantic segmentation net-work for natural history collection specimens

Nieva De La Hidalga, Abraham ORCID:, Rosin, Paul ORCID:, Sun, Xianfang ORCID:, Livermore, Laurence, Durran, James, Turner, James, Dillen, Mathias, Musson, Alicia, Phillips, Sarah, Groom, Quentin and Hardisty, Alex ORCID: 2022. Cross-validation of a semantic segmentation net-work for natural history collection specimens. Machine Vision and Applications 33 , 39. 10.1007/s00138-022-01276-z

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Semantic segmentation has been proposed as a tool to accelerate the processing of natural history collection images. However, developing a flexible and resilient segmentation network requires an approach for adaptation which allows processing different datasets with minimal training and validation. This paper presents a cross-validation approach designed to determine whether a semantic segmentation network possesses the flexibility required for application across different collections and institutions. Consequently, the specific objectives of cross-validating the semantic segmentation network are to (a) evaluate the effectiveness of the network for segmenting image sets derived from collections different from the one in which the network was initially trained on; and (b) test the adaptability of the segmentation network for use in other types of collections. The resilience to data variations from different institutions and the portability of the network across different types of collections are required to confirm its general applicability. The proposed validation method is tested on the Natural History Museum semantic segmentation network, designed to process entomological microscope slides. The proposed semantic segmentation network is evaluated through a series of cross-validation experiments designed to test using data from two types of collections: microscope slides (from three institutions) and herbarium sheets (from seven institutions). The main contribution of this work is the method, software and ground truth sets created for this cross-validation as they can be reused in testing similar segmentation proposals in the context of digitization of natural history collections. The cross-validation of segmentation methods should be a required step in the integration of such methods into image processing workflows for natural history collections.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Chemistry
Computer Science & Informatics
Publisher: Springer Verlag
ISSN: 0932-8092
Date of First Compliant Deposit: 21 February 2022
Date of Acceptance: 25 December 2021
Last Modified: 05 Jan 2024 02:34

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