|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Facing the Void: Overcoming Missing Data in Multi-View Imagery|
Pereira, Matheus B.
Dos Santos, Jefersson A.
Multi-Modal Machine Learning
Multi-view Missing Data Completion
|Citation:||Machado G, Pereira MB, Nogueira K & Dos Santos JA (2022) Facing the Void: Overcoming Missing Data in Multi-View Imagery. <i>IEEE Access</i>. https://doi.org/10.1109/access.2022.3231617|
|Abstract:||In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from images presenting the same object from multiple perspectives (or views) in order to enhance the general scene understanding and, consequently, increase the performance. However, this task, commonly called multi-view image classification, has a major challenge: missing data. In this paper, we propose a novel technique for multi-view image classification robust to this problem. The proposed method, based on state-of-the-art deep learning-based approaches and metric learning, can be easily adapted and exploited in other applications and domains. A systematic evaluation of the proposed algorithm was conducted using two multi-view aerial-ground datasets with very distinct properties. Results show that the proposed algorithm provides improvements in multi-view image classification accuracy when compared to state-of-the-art methods. The code of the proposed approach is available at https://github.com/Gabriellm2003/remote_sensing_missing_data.|
|Rights:||This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/|
|Notes:||Output Status: Forthcoming/Available Online|
|Facing_the_Void_Overcoming_Missing_Data_in_Multi-View_Imagery.pdf||Fulltext - Published Version||19.24 MB||Adobe PDF||View/Open|
This item is protected by original copyright
A file in this item is licensed under a Creative Commons License
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.