Reinterpreting the Application of Gabor Filters as a Manipulation of the Margin in Linear Support Vector Machines
Issue No. 07 - July (2010 vol. 32)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.75
Simon Lucey , Commonwealth Science and Industrial Research Organization (CSIRO), Australia
Tsuhan Chen , Carnegie Mellon University, Pittsburgh and Cornell University, Ithaca
Ahmed Bilal Ashraf , Carnegie Mellon University, Pittsburgh
Linear filters are ubiquitously used as a preprocessing step for many classification tasks in computer vision. In particular, applying Gabor filters followed by a classification stage, such as a support vector machine (SVM), is now common practice in computer vision applications like face identity and expression recognition. A fundamental problem occurs, however, with respect to the high dimensionality of the concatenated Gabor filter responses in terms of memory requirements and computational efficiency during training and testing. In this paper, we demonstrate how the preprocessing step of applying a bank of linear filters can be reinterpreted as manipulating the type of margin being maximized within the linear SVM. This new interpretation leads to sizable memory and computational advantages with respect to existing approaches. The reinterpreted formulation turns out to be independent of the number of filters, thereby allowing the examination of the feature spaces derived from arbitrarily large number of linear filters, a hitherto untestable prospect. Further, this new interpretation of filter banks gives new insights, other than the often cited biological motivations, into why the preprocessing of images with filter banks, like Gabor filters, improves classification performance.
Gabor filters, support vector machine, maximum margin, expression recognition.
Simon Lucey, Tsuhan Chen, Ahmed Bilal Ashraf, "Reinterpreting the Application of Gabor Filters as a Manipulation of the Margin in Linear Support Vector Machines", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1335-1341, July 2010, doi:10.1109/TPAMI.2010.75