Subspace learning from image gradient orientations

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Title: Subspace learning from image gradient orientations
Author(s): Tzimiropoulos, G
Zafeiriou, S
Pantic, M
Item Type: Journal Article
Abstract: We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data are typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing pixel intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Within this framework, which we coin Image Gradient Orientations (IGO) subspace learning, we first formulate and study the properties of Principal Component Analysis of image gradient orientations (IGO-PCA). We then show its connection to previously proposed robust PCA techniques both theoretically and experimentally. Finally, we derive a number of other popular subspace learning techniques, namely, Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE), and Laplacian Eigenmaps (LE). Experimental results show that our algorithms significantly outperform popular methods such as Gabor features and Local Binary Patterns and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition. In addition to this, the proposed IGO-methods require the eigendecomposition of simple covariance matrices and are as computationally efficient as their corresponding ℓ2 norm intensity-based counterparts. Matlab code for the methods presented in this paper can be found at http://ibug.doc.ic.ac.uk/resources.
Publication Date: 24-Jan-2012
Date of Acceptance: 1-Jan-2012
URI: http://hdl.handle.net/10044/1/43175
DOI: http://dx.doi.org/10.1109/TPAMI.2012.40
ISSN: 0162-8828
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 2454
End Page: 2466
Journal / Book Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 34
Issue: 12
Copyright Statement: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
ENGINEERING, ELECTRICAL & ELECTRONIC
Image gradient orientations
robust principal component analysis
discriminant analysis
nonlinear dimensionality reduction
face recognition
FACE-RECOGNITION ALGORITHMS
DIMENSIONALITY REDUCTION
DISCRIMINANT-ANALYSIS
SPARSE REPRESENTATION
FEATURE-EXTRACTION
ILLUMINATION
LAPLACIANFACES
REGISTRATION
EIGENFACES
PROJECTION
Artificial Intelligence & Image Processing
0801 Artificial Intelligence And Image Processing
0806 Information Systems
0906 Electrical And Electronic Engineering
Appears in Collections:Faculty of Engineering
Computing



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