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Type: Conference paper
Title: On the importance of pair-wise feature correlations for image classification
Author: McDonnell, M.
McKilliam, R.
De Chazal, P.
Citation: Proceedings of International Joint Conference on Neural Networks, 2016, vol.2016-October, pp.2290-2297
Publisher: IEEE
Publisher Place:
Issue Date: 2016
Series/Report no.: IEEE International Joint Conference on Neural Networks (IJCNN)
ISBN: 9781509006199
ISSN: 2161-4393
Conference Name: International Joint Conference on Neural Networks (IJCNN 2016) (24 Jul 2016 - 29 Jul 2016 : Vancouver, CANADA)
Statement of
Mark D. McDonnell, Robby G. McKilliam, Philip de Chazal
Abstract: We show that simple linear classification of pairwise products of convolutional features achieves near state-of-the-art performance on some standard labelled image databases. Specifically, we found test classification error rates on the MNIST handwritten digits image database of under 0.5%, and achieved under 19% and under 44% error rates on the CIFAR-10 and CIFAR-100 RGB image databases. Since the number of weights in such a classifier grows with the square of the number of features, we discuss how implementation of such a pair-wise products classifier can be achieved in an SLFN architecture where the hidden unit function is the simple quadratic nonlinearity: we can this a Quadratic Neural Network (QNN). We compare this method to setting the input weights in a QNN randomly, and find optimal performance can be achieved provided the hidden layer is sufficiently large. This analysis provides insight on why `extreme-learning machines' can achieve classification performance equal to or better than the use of backpropagation training.
Description: IJCNN 2016 (as part of WCCI 2016)
Rights: ©2016 IEEE
DOI: 10.1109/IJCNN.2016.7727483
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Electrical and Electronic Engineering publications

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