Subspace learning from image gradient orientations
We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the l...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers
2012
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/31424/ |
| _version_ | 1848794198352855040 |
|---|---|
| author | Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja |
| author_facet | Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja |
| author_sort | Tzimiropoulos, Georgios |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the low-dimensional subspace of a given data population. We show that replacing pixel intensities with gradient orientations and the 2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Within this framework, which we coin IGO (Image Gradient Orientations) 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 outperform significantly 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 eigen- ecomposition of simple covariance matrices and are as computationally efficient as their corresponding 2 norm intensity-based counterparts. Matlab code for the methods presented in this paper can be found at http://ibug.doc.ic.ac.uk/resources |
| first_indexed | 2025-11-14T19:12:23Z |
| format | Article |
| id | nottingham-31424 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:23Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314242020-05-04T20:21:07Z https://eprints.nottingham.ac.uk/31424/ Subspace learning from image gradient orientations Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the low-dimensional subspace of a given data population. We show that replacing pixel intensities with gradient orientations and the 2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Within this framework, which we coin IGO (Image Gradient Orientations) 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 outperform significantly 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 eigen- ecomposition of simple covariance matrices and are as computationally efficient as their corresponding 2 norm intensity-based counterparts. Matlab code for the methods presented in this paper can be found at http://ibug.doc.ic.ac.uk/resources Institute of Electrical and Electronics Engineers 2012-12 Article PeerReviewed Tzimiropoulos, Georgios, Zafeiriou, Stefanos and Pantic, Maja (2012) Subspace learning from image gradient orientations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (12). pp. 2454-2466. ISSN 1939-3539 image gradient orientations robust principal component analysis discriminant analysis non-linear dimensionality reduction face recognition http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6138864 doi:10.1109/TPAMI.2012.40 doi:10.1109/TPAMI.2012.40 |
| spellingShingle | image gradient orientations robust principal component analysis discriminant analysis non-linear dimensionality reduction face recognition Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja Subspace learning from image gradient orientations |
| title | Subspace learning from image gradient orientations |
| title_full | Subspace learning from image gradient orientations |
| title_fullStr | Subspace learning from image gradient orientations |
| title_full_unstemmed | Subspace learning from image gradient orientations |
| title_short | Subspace learning from image gradient orientations |
| title_sort | subspace learning from image gradient orientations |
| topic | image gradient orientations robust principal component analysis discriminant analysis non-linear dimensionality reduction face recognition |
| url | https://eprints.nottingham.ac.uk/31424/ https://eprints.nottingham.ac.uk/31424/ https://eprints.nottingham.ac.uk/31424/ |