Wireless Image Retrieval at the Edge

We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server. These can be images of the same person or a vehicle taken from other cameras at different times and locations. Our goal is to maximize...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 39; no. 1; pp. 89 - 100
Main Authors: Jankowski, Mikolaj, Gunduz, Deniz, Mikolajczyk, Krystian
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Ablation
Artificial neural networks
Bandwidths
Deep learning
Feature extraction
Image coding
Image compression
Image edge detection
Image management
Image quality
Image retrieval
Image transmission
Internet of Things
joint source-channel coding
Performance evaluation
person re-identification
Servers
Task analysis
Wireless communication
ISSN:0733-8716, 1558-0008
Online Access:Get full text
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Summary:We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server. These can be images of the same person or a vehicle taken from other cameras at different times and locations. Our goal is to maximize the accuracy of the retrieval task under power and bandwidth constraints over the wireless link. Due to the stringent delay constraint of the underlying application, sending the whole image at a sufficient quality is not possible. We propose two alternative schemes based on digital and analog communications, respectively. In the digital approach, we first propose a deep neural network (DNN) aided retrieval-oriented image compression scheme, whose output bit sequence is transmitted over the channel using conventional channel codes. In the analog joint source and channel coding (JSCC) approach, the feature vectors are directly mapped into channel symbols. We evaluate both schemes on image based re-identification (re-ID) tasks under different channel conditions, including both static and fading channels. We show that the JSCC scheme significantly increases the end-to-end accuracy, speeds up the encoding process, and provides graceful degradation with channel conditions. The proposed architecture is evaluated through extensive simulations on different datasets and channel conditions, as well as through ablation studies.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.3036955