Heterogeneous multi-column ConvNets with a fusion framework for object recognition

Y. Li; Ferdous Sohel; Mohammed Bennamoun; H. Lei

doi:10.1109/WACV.2015.108

Heterogeneous multi-column ConvNets with a fusion framework for object recognition

Y. Li, Ferdous Sohel, Mohammed Bennamoun, H. Lei

Oceans Graduate School

Research output: Chapter in Book/Conference paper › Conference paper

2 Citations (Scopus)

Abstract

© 2015 IEEE. The purpose of this paper is to investigate heterogeneous multi-column ConvNets (MCCNN) and fusion methods for them. We first construct heterogeneous MCCNN by combining ConvNets with different structures. We then use different fusion methods to check their performances to find out the effect of fusion methods for MCCNN. We also propose a novel sliding window based fusion framework which defines a specific subset of columns to be picked up from MCCNN for fusion. Two different strategies (exhaustive sliding window and sliding window from training) are investigated to determine the best performance of the fusion process. We tested the heterogeneous MCCNN and sliding window fusion on the MNIST dataset for optical character recognition. Experiments show that MCCNN improved the accuracy of recognition compared with a single column of ConvNets. Moreover, sliding window fusion is a more generalized fusion method and consistently achieves better results compared with the traditional fusion methods. We also tested the MCCNN and sliding window fusion on CIFAR-10 and Caltech-256 datasets. We achieved superior results compared to existing state-of-the-art techniques.

Original language	English
Title of host publication	2015 IEEE Winter Conference on Applications of Computer Vision
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	773-780
Volume	N/A
ISBN (Print)	9781479966820
DOIs	https://doi.org/10.1109/WACV.2015.108
Publication status	Published - 2015
Event	2015 IEEE Winter Conference on Applications of Computer Vision - Waikoloa, United States Duration: 5 Jan 2015 → 9 Mar 2015

Conference

Conference	2015 IEEE Winter Conference on Applications of Computer Vision
Country	United States
City	Waikoloa
Period	5/01/15 → 9/03/15

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10.1109/WACV.2015.108

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Cite this

@inproceedings{675fb493eed043d9a9c59f3a55ecf76d,

title = "Heterogeneous multi-column ConvNets with a fusion framework for object recognition",

abstract = "{\textcopyright} 2015 IEEE. The purpose of this paper is to investigate heterogeneous multi-column ConvNets (MCCNN) and fusion methods for them. We first construct heterogeneous MCCNN by combining ConvNets with different structures. We then use different fusion methods to check their performances to find out the effect of fusion methods for MCCNN. We also propose a novel sliding window based fusion framework which defines a specific subset of columns to be picked up from MCCNN for fusion. Two different strategies (exhaustive sliding window and sliding window from training) are investigated to determine the best performance of the fusion process. We tested the heterogeneous MCCNN and sliding window fusion on the MNIST dataset for optical character recognition. Experiments show that MCCNN improved the accuracy of recognition compared with a single column of ConvNets. Moreover, sliding window fusion is a more generalized fusion method and consistently achieves better results compared with the traditional fusion methods. We also tested the MCCNN and sliding window fusion on CIFAR-10 and Caltech-256 datasets. We achieved superior results compared to existing state-of-the-art techniques.",

author = "Y. Li and Ferdous Sohel and Mohammed Bennamoun and H. Lei",

year = "2015",

doi = "10.1109/WACV.2015.108",

language = "English",

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pages = "773--780",

booktitle = "2015 IEEE Winter Conference on Applications of Computer Vision",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2015 IEEE Winter Conference on Applications of Computer Vision ; Conference date: 05-01-2015 Through 09-03-2015",

}

Li, Y, Sohel, F , Bennamoun, M & Lei, H 2015, Heterogeneous multi-column ConvNets with a fusion framework for object recognition. in 2015 IEEE Winter Conference on Applications of Computer Vision. vol. N/A, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 773-780, 2015 IEEE Winter Conference on Applications of Computer Vision , Waikoloa, United States, 5/01/15. https://doi.org/10.1109/WACV.2015.108

Heterogeneous multi-column ConvNets with a fusion framework for object recognition. / Li, Y.; Sohel, Ferdous ; Bennamoun, Mohammed; Lei, H.

2015 IEEE Winter Conference on Applications of Computer Vision. Vol. N/A United States : IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 773-780.

Research output: Chapter in Book/Conference paper › Conference paper

TY - GEN

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N2 - © 2015 IEEE. The purpose of this paper is to investigate heterogeneous multi-column ConvNets (MCCNN) and fusion methods for them. We first construct heterogeneous MCCNN by combining ConvNets with different structures. We then use different fusion methods to check their performances to find out the effect of fusion methods for MCCNN. We also propose a novel sliding window based fusion framework which defines a specific subset of columns to be picked up from MCCNN for fusion. Two different strategies (exhaustive sliding window and sliding window from training) are investigated to determine the best performance of the fusion process. We tested the heterogeneous MCCNN and sliding window fusion on the MNIST dataset for optical character recognition. Experiments show that MCCNN improved the accuracy of recognition compared with a single column of ConvNets. Moreover, sliding window fusion is a more generalized fusion method and consistently achieves better results compared with the traditional fusion methods. We also tested the MCCNN and sliding window fusion on CIFAR-10 and Caltech-256 datasets. We achieved superior results compared to existing state-of-the-art techniques.

AB - © 2015 IEEE. The purpose of this paper is to investigate heterogeneous multi-column ConvNets (MCCNN) and fusion methods for them. We first construct heterogeneous MCCNN by combining ConvNets with different structures. We then use different fusion methods to check their performances to find out the effect of fusion methods for MCCNN. We also propose a novel sliding window based fusion framework which defines a specific subset of columns to be picked up from MCCNN for fusion. Two different strategies (exhaustive sliding window and sliding window from training) are investigated to determine the best performance of the fusion process. We tested the heterogeneous MCCNN and sliding window fusion on the MNIST dataset for optical character recognition. Experiments show that MCCNN improved the accuracy of recognition compared with a single column of ConvNets. Moreover, sliding window fusion is a more generalized fusion method and consistently achieves better results compared with the traditional fusion methods. We also tested the MCCNN and sliding window fusion on CIFAR-10 and Caltech-256 datasets. We achieved superior results compared to existing state-of-the-art techniques.

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BT - 2015 IEEE Winter Conference on Applications of Computer Vision

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