Facing the Void: Overcoming Missing Data in Multi-View Imagery

Citation

Machado G, Pereira MB, Nogueira K & Dos Santos JA (2022) Facing the Void: Overcoming Missing Data in Multi-View Imagery. IEEE Access. 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.

Keywords
Remote Sensing; Image Classification; Multi-Modal Machine Learning; Metric Learning; Cross-View Matching; Multi-view Missing Data Completion

Notes
Output Status: Forthcoming/Available Online

Journal
IEEE Access

• Facing The Void Overcoming Missing Data In Multi-View Imagery