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Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC)
Metadata Last Update2020: (UTC) administrator
Citation KeyFernandezLopeHira:2015:ImSeAs
TitleImage Segmentation Assessment from the Perspective of a Higher Level Task
Access Date2021, Dec. 07
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Author1 Fernandez, Mariela Atausinchi
2 Lopes, Rubens M.
3 Hirata, Nina S. T.
Affiliation1 Institute of Mathematics and Statistics - University of São Paulo
2 Oceanographic Institute - University of São Paulo
3 Institute of Mathematics and Statistics - University of São Paulo
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 21:04:28 :: -> administrator ::
2020-02-19 02:14:03 :: administrator -> :: 2015
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Content TypeExternal Contribution
Keywordsholistic system evaluation
classification evaluation
plankton image classification
segmentation evaluation
plankton image segmentation
AbstractImage segmentation evaluation is usually performed by visual inspection, by comparing segmentation to a ground-truth, or by computing an objective function value for the segmented image. All these methods require user participation either for manual evaluation, or to define ground-truth, or to embed desired segmentation properties into the objective function. However, evaluating segmentation is a hard task if none of these three methods can be easily employed. Often, higher level tasks such as detecting or classifying objects can be performed much more easily than low level tasks such as delineating the contours of the objects. This fact can be advantageously used to evaluate algorithms for a low level task. We apply this approach to a case study on plankton classification. Segmentation methods are evaluated from the perspective of plankton classification accuracy. This approach not only helps choosing a good segmentation method but also helps detecting points where segmentation is failing. In addition, this more holistic form of segmentation evaluation better meets requirements of big data analysis. > SDLA > SIBGRAPI 2015 > Image Segmentation Assessment...
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