DOIONLINE

DOIONLINE NO - IJASEAT-IRAJ-DOIONLINE-14260

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International Journal of Advances in Science, Engineering and Technology(IJASEAT)-IJASEAT
Journal Home
Volume Issue
Issue
Volume-6, Issue-4  ( Oct, 2018 )
Paper Title
Multi-Scale Fully Convolutional Neural Networks for Classification of Visual Objects
Author Name
Shu-Mei Lin, Hsueh-Fu Lu, Yuan-Hsiang Chang
Affilition
Dept. of Information and Computer Engineering Chung Yuan Christian University, Taiwan, R.O.C.
Pages
1-5
Abstract
Recent studies have shown great potentials of the Convolutional Neural Networks (CNNs) to yield excellent results on visual classification tasks. While the CNNs could achieve translation-invariance by spatial convolution and pooling mechanisms, their ability to achieve scale-invariance is still limited. To overcome the challenge, we propose a multiscale fully CNNs network architecture that constitutes three types of multi-scale fusions, namely: (1) multi-size filters fusion; (2) multi-layer features fusion; and (3) multi-resolution I/Os fusion. Our CNNs’ architecture is designed to incorporate the fusions such that scale-invariance could be achieved. Using the CIFAR-10 and CIFAR-100 datasets as the benchmark for testing, our architecture has achieved classification accuracy with 96.6% (CIFAR-10) and 80.36% (CIFAR- 100), respectively. In conclusion, our multi-scale fully CNNs architecture has demonstrated the state-of-art classification performance based on published works to date. Index terms- Convolutional Neural Networks, CNN, multi-size kernel fusion, multi-layer feature fusion, multi-resolution I/O fusion.
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