SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters

Erik Ostrowski, Bharath Srinivas Prabakaran, Muhammad Shafique

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Achieving high-quality semantic segmentation predictions using only image-level labels enables a new level of real-world applicability. Although state-of-the-art networks deliver reliable predictions, the amount of handcrafted pixel-wise annotations to enable these results are not feasible in many real-world applications. Hence, several works have already targeted this bottleneck, using classifier-based networks like Class Activation Maps [1] (CAMs) as a base. Addressing CAM's weaknesses of fuzzy borders and incomplete predictions, state-of-the-art approaches rely only on adding regulations to the classifier loss or using pixel-similarity-based refinement after the fact. We propose a framework that introduces an additional module using object perimeters for improved saliency. We define object perimeter information as the line separating the object and background. Our new PerimeterFit module will be applied to pre-refine the CAM predictions before using the pixel-similarity-based network. In this way, our PerimeterFit increases the quality of the CAM prediction while simultaneously improving the false negative rate. We investigated a wide range of state-of-the-art unsupervised semantic segmentation networks and edge detection techniques to create useful perimeter maps, which enable our framework to predict object locations with sharper perimeters. We achieved up to 1.5% improvement over frameworks without our PerimeterFit module. We conduct an exhaustive analysis to illustrate that SILOP enhances existing state-of-the-art frameworks for image-level-based semantic segmentation. The framework is open-source and accessible online at

Original languageEnglish (US)
Title of host publicationIJCNN 2023 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665488679
StatePublished - 2023
Event2023 International Joint Conference on Neural Networks, IJCNN 2023 - Gold Coast, Australia
Duration: Jun 18 2023Jun 23 2023

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2023 International Joint Conference on Neural Networks, IJCNN 2023
CityGold Coast


  • Class Activation Maps
  • Image-level supervision
  • Semantic Segmentation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


Dive into the research topics of 'SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters'. Together they form a unique fingerprint.

Cite this