Author
Özer, Sedat, Ege, M., Özkanoglu, M. A.
Publication Date
2022-09
Publication Place
-
Elsevier
Subject
Efficient learning, Multi-modal fusion, Multi-temporal fusion
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0031-3203
Record ID
55adc5dc-a7b3-43cc-b100-b75f240fb973
Library Location
Computer Science
Date
2022-09
Notes
TÜBİTAK
Sample Text
Recent developments in pattern analysis have motivated many researchers to focus on developing deep learning based solutions in various image processing applications. Fusing multi-modal images has been one such application area where the interest is combining different information coming from different modalities in a more visually meaningful and informative way. For that purpose, it is important to first extract salient features from each modality and then fuse them as efficiently and informatively as possible. Recent literature on fusing multi-modal images reports multiple deep solutions that combine both visible (RGB) and infra-red (IR) images. In this paper, we study the performance of various deep solutions available in the literature while seeking an answer to the question: “Do we really need deeper networks to fuse multi-modal images?” To have an answer for that question, we introduce a novel architecture based on Siamese networks to fuse RGB (visible) images with infrared (IR) images and report the state-of-the-art results. We present an extensive analysis on increasing the layer numbers in the architecture with the above-mentioned question in mind to see if using deeper networks (or adding additional layers) adds significant performance in our proposed solution. We report the state-of-the-art results on visually fusing given visible and IR image pairs in multiple performance metrics, while requiring the least number of trainable parameters. Our experimental results suggest that shallow networks (as in our proposed solutions in this paper) can fuse both visible and IR images as well as the deep networks that were previously proposed in the literature (we were able to reduce the total number of trainable parameters up to 96.5%, compare 2,625 trainable parameters to the 74,193 trainable parameters).
DOI
10.1016/j.patcog.2022.108712
Cilt
129
