References

@conference{Stojnev2024,

title = {Superpixel Based Region Segmentation Using Shallow Classifiers},

author = {Dragana Stojnev and Masa Jovanovic and Nikola Blagojevic and Andelija Mladenovic and Igor Mihajlovic and David Nikolic and Dragan Jankovic},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192197065\&doi=10.1109%2fINFOTEH60418.2024.10496023\&partnerID=40\&md5=13c66196b8100f6fafe39671edff1ccb},

doi = {10.1109/INFOTEH60418.2024.10496023},

year  = {2024},

date = {2024-01-01},

journal = {2024 23rd International Symposium INFOTEH-JAHORINA, INFOTEH 2024 - Proceedings},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This paper deals with solving the problem of semantic image segmentation in scenarios characterized by limited availability of annotated data which is common in many fields such as remote sensing, medical imaging, agriculture, etc. An approach that maps superpixels to features and uses a shallow supervised classifier is proposed. It emphasizes efficiency and context-aware segmentation, using a superpixel-based methodology. In experiments on a floodplain segmentation dataset, the algorithm shows favorable outcomes. Promising results from preliminary experiments in medical imaging also suggest potential applications in other fields. © 2024 IEEE.},

keywords = {Classification (of information); Medical imaging; Remote sensing; Semantic Segmentation; Semantics; Context-Aware; Feature; Flood plains; Region segmentation; Remote-sensing; Semantic image segmentations; Shallow classifier; Super pixels; Supervised classifiers; Superpixels},

pubstate = {published},

tppubtype = {conference}

}