Explicit discriminative representation for improved classification of manifold features

Wiliem, Arnold, Vemulapalli, Raviteja and Lovell, Brian C. (2016) Explicit discriminative representation for improved classification of manifold features. Pattern Recognition Letters, 80 121-128. doi:10.1016/j.patrec.2016.06.006

Author Wiliem, Arnold
Vemulapalli, Raviteja
Lovell, Brian C.
Title Explicit discriminative representation for improved classification of manifold features
Journal name Pattern Recognition Letters   Check publisher's open access policy
ISSN 0167-8655
Publication date 2016-06-22
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.patrec.2016.06.006
Open Access Status Not Open Access
Volume 80
Start page 121
End page 128
Total pages 8
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Collection year 2017
Language eng
Formatted abstract
We tackle the problem of extracting explicit discriminative feature representation for manifold features. Manifold features have already been shown to have excellent performance in a number of image/video classification tasks. Nevertheless, as most manifold features lie in a non-Euclidean space, the existing machineries operating in Euclidean space are not applicable. The proposed explicit feature representation enables us to use the existing Euclidean machineries, significantly reducing the challenges of processing manifold features. To that end, we first embed the manifold features into a Reproducing Kernel Hilbert Space that can encode the manifold geometry. Then, we extract the explicit representation by using the empirical kernel feature space, an explicit lower dimensional space wherein the inner product is equivalent to the corresponding kernel similarity. The final feature representation is then derived from a linear combination of multiple explicit representations from various manifold kernels. We propose a max-margin approach to learn an effective linear combination that will improve the feature discriminative power. Evaluations in various image classification tasks show that the proposed approach consistently and significantly outperforms recent state-of-the-art methods
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Created: Tue, 05 Jul 2016, 09:42:56 EST by Arnold Wiliem on behalf of School of Information Technol and Elec Engineering