Adaptive weights learning in CNN feature fusion for crime scene investigation image classification

- Ying, Liu, Qian Nan, Zhang, Fu Ping, Wang, Tuan Kiang, Chiew, Keng Pang, Lim, Heng Chang, Zhang, Lu, Chao, Jun, Lu, Nam, Ling

Title
Adaptive weights learning in CNN feature fusion for crime scene investigation image classification
Creator
Ying, Liu; Qian Nan, Zhang; Fu Ping, Wang; Tuan Kiang, Chiew; Keng Pang, Lim; Heng Chang, Zhang; Lu, Chao; Jun, Lu; Nam, Ling
Date
2021
Type
Text; Journal article
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186057
Identifier
vital:16839
Identifier
https://doi.org/10.1080/09540091.2021.1875987
Identifier
ISBN:0954-0091 (ISSN)
Abstract
The combination of features from the convolutional layer and the fully connected layer of a convolutional neural network (CNN) provides an effective way to improve the performance of crime scene investigation (CSI) image classification. However, in existing work, as the weights in feature fusion do not change after the training phase, it may produce inaccurate image features which affect classification results. To solve this problem, this paper proposes an adaptive feature fusion method based on an auto-encoder to improve classification accuracy. The method includes the following steps: Firstly, the CNN model is trained by transfer learning. Next, the features of the convolution layer and the fully connected layer are extracted respectively. These extracted features are then passed into the auto-encoder for further learning with Softmax normalisation to obtain the adaptive weights for performing final classification. Experiments demonstrated that the proposed method achieves higher CSI image classification performance compared with fix weights feature fusion. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Publisher
Taylor and Francis Ltd.
Relation
Connection Science Vol. 33, no. 3 (2021), p. 719-734
Rights
All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights
https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights
Copyright © 2021 Informa UK Limited
Rights
Open Access
Subject
46 Information and computing sciences; Auto-encoder; Convolutional neural network; Crime scene investigation image classification; Feature fusion
Full Text
Reviewed
Funder
This work has been partially supported by National Natural Science Foundation of China [grant numbers 61802305, 61671377].
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