CS-Fnet: A Compressive Sampling Frequency Neural Network for Simultaneous Image Compression and Recognition

Rui Ma; Qi Hao

10.1109/MFI52462.2021.9591201

2021-11

2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

978-1-6654-4522-1

2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

1-6

23-25 Sept. 2021

Karlsruhe, Germany

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Data recognition using compressive measurements is desired for intelligent edge devices to save computation and communication resources. However, direct recognition of compressed image data is often difficult because the compression operation disturbs the original signal structure. In compressive sensing (CS), original signals are transformed into the frequency domain or other domains for sparse representations. This paper presents a compressive sampling frequency neural network (CS-Fnet) to achieve high computational efficiency for compressed image recognition, whose measurement matrix (MM) is automatically obtained through the CS-Fnet training. Furthermore, the MM is constructed in the form of the Kronecker product, which can reduce the number of MM parameters, and hence the CS-Fnet training can achieve much higher computational efficiency and convergence speed. The proposed method is validated using the MNIST dataset and gesture datasets. The experiment results demonstrate that the proposed CS-Fnet outperforms traditional convolution neural networks (CNNs) in terms of image recognition accuracy, and the learned MMs yield higher reconstruction accuracy than traditional MMs.

Training Image coding Frequency-domain analysis Image edge detection Neural networks Hardware Computational efficiency

第一

英语

CPCI-S ; EI

National Natural Science Foundation of China[61773197]

Computer Science ; Engineering ; Mathematics

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Mathematics, Applied

WOS:000853882500042

人工提交

The Department of Computer Science and Engineering, and the Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China

Rui Ma,Qi Hao. CS-Fnet: A Compressive Sampling Frequency Neural Network for Simultaneous Image Compression and Recognition[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:1-6.