V1-inspired features induce a weighted margin in SVMs

Bristow, Hilton & Lucey, Simon (2012) V1-inspired features induce a weighted margin in SVMs. In Lecture Notes in Computer Science : Proceedings of the 12th European Conference on Computer Vision : Part II, Springer, Florence, Italy, pp. 59-72.

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Image representations derived from simplified models of the primary visual cortex (V1), such as HOG and SIFT, elicit good performance in a myriad of visual classification tasks including object recognition/detection, pedestrian detection and facial expression classification. A central question in the vision, learning and neuroscience communities regards why these architectures perform so well. In this paper, we offer a unique perspective to this question by subsuming the role of V1-inspired features directly within a linear support vector machine (SVM). We demonstrate that a specific class of such features in conjunction with a linear SVM can be reinterpreted as inducing a weighted margin on the Kronecker basis expansion of an image. This new viewpoint on the role of V1-inspired features allows us to answer fundamental questions on the uniqueness and redundancies of these features, and offer substantial improvements in terms of computational and storage efficiency.

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ID Code: 57843
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: V1, SVM, Histograms of Oriented Gradients, Support Vector Machine, Features
DOI: 10.1007/978-3-642-33709-3_5
ISBN: 9783642337093
ISSN: 1611-3349
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2012 Springer
Copyright Statement: The original publication is available at SpringerLink
Deposited On: 06 Mar 2013 22:41
Last Modified: 14 May 2015 22:17

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