Paper Info
Title
Reconfigurable filter bank design via principal component analysis and low rank approximation
Abstract
Feature extraction plays an important role in signal processing, computer vision, etc., and filter bank is used to comprehensively delineate the characteristics of input signals from different perspectives. Nonetheless, more detailed extraction of information necessitates the application of more filters with resulting increase in computational load in proportion to the number of filters. To alleviate computation load, this paper proposes a reconfigurable filter bank design that exploits symmetrical properties in coefficients of filter bank to share computations. The proposed method is based on principal component analysis that projects filter coefficients onto a more symmetrical vector space whereas low rank approximation while trading off between accuracy and computational efficiency, discards less important components to further mitigate computation load. This paper demonstrates a case study on Gabor filter bank, which is composed of 16 filter kernels. The experiments show that we reduce 68% additions and 78% multiplications in comparison with naive convolution process; and reduce 40% additions and 57% multiplications in comparison with conventional 2D-Gabor filter implementation.
Year
DOI
Venue
2015
10.1109/GlobalSIP.2015.7418240
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Keywords
Field
DocType
Convolution,low rank approximation,principal component analysis
Signal processing,Pattern recognition,Filter bank,Network synthesis filters,Algorithm,Filter (signal processing),Composite image filter,Artificial intelligence,Kernel adaptive filter,Adaptive filter,Mathematics,Filter design
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
5
Name
Order
Citations
PageRank
Chun-Hsi Huang100.68
Gwo Giun (Chris) Lee252.17
Chun-Fu Chen366.52
Xia, Yinglong421726.91
Ching-yung Lin51963175.16