Computer vision plays a crucial role in Advanced Assistance Systems. Most computer vision systems are
based on Deep Convolutional Neural Networks (deep CNN) architectures. However, the high
computational resource to run a CNN algorithm is demanding. Therefore, the methods to speed up
computation have become a relevant research issue. Even though several works on architecture reduction
found in the literature have not yet been achieved satisfactory results for embedded real-time system
applications. This paper presents an alternative approach based on the Multilinear Feature Space (MFS)
method resorting to transfer learning from large CNN architectures. The proposed method uses CNNs to
generate feature maps, although it does not work as complexity reduction approach. After the training
process, the generated features maps are used to create vector feature space. We use this new vector space
to make projections of any new sample to classify them. Our method, named AMFC, uses the transfer
learning from pre-trained CNN to reduce the classification time of new sample image, with minimal
accuracy loss. Our method uses the VGG-16 model as the base CNN architecture for experiments;
however, the method works with any similar CNN model. Using the well-known Vehicle Image Database
and the German Traffic Sign Recognition Benchmark, we compared the classification time of the original
VGG-16 model with the AMFC method, and our method is, on average, 17 times faster. The fast
classification time reduces the computational and memory demands in embedded applications requiring a
large CNN architecture.

User Name : michelvinagreiro
Posted 17-10-2021 on 06:03:22 AEDT

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