Conference Paper2021

Attention toward neighbors: A context aware framework for high resolution image segmentation

Fahim Faisal Niloy, M Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman

2021 IEEE International Conference on Image Processing (ICIP)

IEEE, pp. 2279–2283

DOI: 10.1109/icip42928.2021.9506194Open AccessURL

Abstract

High-resolution image segmentation remains challenging and error-prone due to the enormous size of intermediate feature maps. Conventional methods avoid this problem by using patch based approaches where each patch is segmented independently. However, independent patch segmentation induces errors, particularly at the patch boundary due to the lack of contextual information in very high-resolution images where the patch size is much smaller compared to the full image. To overcome these limitations, in this paper, we propose a novel framework to segment a particular patch by incorporating contextual information from its neighboring patches. This allows the segmentation network to see the target patch with a wider field of view without the need of larger feature maps. Comparative analysis from a number of experiments shows that our proposed framework is able to segment high resolution images with significantly improved mean Intersection over Union and overall accuracy.

Keywords

  • context-aware segmentation
  • very high resolution imagery
  • contextual information

CCDS Authors

Fahim Faisal Niloy
Fahim Faisal Niloy

Fahim Faisal Niloy

  • Research Assistant, CCDS

PhD Student

University of California Riverside

Published at

WACV, ICPR, ICIP, Pattern Recognition

Research Interests

Machine Learning

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

AKM Mahbubur Rahman, PhD
AKM Mahbubur Rhman, PhD

AKM Mahbubur Rahman, PhD

  • Director, DS & IPT Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, ECCV, WACV, PAKDD, LREC, IJCNN, ICPR, ICIP, HCII, BEA, Scientific Reports, Information Sciences

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Cited By

  1. 1.Kishor Kumar Bhaumik, Fahim Faisal Niloy, Saif Mahmud, Simon S. Woo. (2024). STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction. Lecture notes in computer science, 288–299[10.1007/978-981-97-2266-2_23]
  2. 2.Kaiwen Du, Xiao Wang, Yan Yan, Yang Lu, Hanzi Wang. (2022). Egnet: A Novel Edge Guided Network for Instance Segmentation. 2022 IEEE International Conference on Image Processing (ICIP), 3868–3872[10.1109/icip46576.2022.9897497]
  3. 3.Enam Ahmed Taufik, Antara Firoz Parsa, Seraj Al Mahmud Mostafa. (2025). Efficient Leaf Disease Classification and Segmentation Using Midpoint Normalization Technique and Attention Mechanism. , 2091–2096[10.1109/icip55913.2025.11084601]
  4. 4.Ankith Jain Rakesh Kumar, Bir Bhanu. (2024). Uncovering Hidden Emotions with Adaptive Multi-Attention Graph Networks. , 4822–4831[10.1109/cvprw63382.2024.00485]
  5. 5.Kevin Kocon, Michel Krämer, Lina Emilie Budde. (2025). Grid-Shift: An Image Preprocessing Approach to Reduce Overfitting in AI Training. , 109–114[10.1109/icfsp67350.2025.11353536]
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