Sparse representations of image gradient orientations for visual recognition and tracking
Recent results [18] have shown that sparse linear representations of a query object with respect to an overcomplete basis formed by the entire gallery of objects of interest can result in powerful image-based object recognition schemes. In this paper, we propose a framework for visual recognition an...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2011
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| Online Access: | https://eprints.nottingham.ac.uk/31413/ |
| _version_ | 1848794195710443520 |
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| 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 | Recent results [18] have shown that sparse linear representations of a query object with respect to an overcomplete basis formed by the entire gallery of objects of interest can result in powerful image-based object recognition schemes. In this paper, we propose a framework for visual recognition and tracking based on sparse representations of image gradient orientations. We show that minimal `1 solutions to problems formulated with gradient orientations can be used for fast and robust object recognition even for probe objects corrupted by outliers. These solutions are obtained without the need for solving the extended problem considered in [18]. We further show that low-dimensional embeddings generated from gradient orientations perform equally well even when probe objects are corrupted by outliers, which, in turn, results in huge computational savings. We demonstrate experimentally that, compared to the baseline method in [18], our formulation results in better recognition rates without the need for block processing and even with smaller number of training samples. Finally, based on our results, we also propose a robust and efficient `1-based “tracking by detection” algorithm. We show experimentally that our tracker outperforms a recently proposed `1-based tracking algorithm in terms of robustness, accuracy and speed. |
| first_indexed | 2025-11-14T19:12:20Z |
| format | Conference or Workshop Item |
| id | nottingham-31413 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:20Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314132020-05-04T20:24:38Z https://eprints.nottingham.ac.uk/31413/ Sparse representations of image gradient orientations for visual recognition and tracking Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja Recent results [18] have shown that sparse linear representations of a query object with respect to an overcomplete basis formed by the entire gallery of objects of interest can result in powerful image-based object recognition schemes. In this paper, we propose a framework for visual recognition and tracking based on sparse representations of image gradient orientations. We show that minimal `1 solutions to problems formulated with gradient orientations can be used for fast and robust object recognition even for probe objects corrupted by outliers. These solutions are obtained without the need for solving the extended problem considered in [18]. We further show that low-dimensional embeddings generated from gradient orientations perform equally well even when probe objects are corrupted by outliers, which, in turn, results in huge computational savings. We demonstrate experimentally that, compared to the baseline method in [18], our formulation results in better recognition rates without the need for block processing and even with smaller number of training samples. Finally, based on our results, we also propose a robust and efficient `1-based “tracking by detection” algorithm. We show experimentally that our tracker outperforms a recently proposed `1-based tracking algorithm in terms of robustness, accuracy and speed. 2011 Conference or Workshop Item PeerReviewed Tzimiropoulos, Georgios, Zafeiriou, Stefanos and Pantic, Maja (2011) Sparse representations of image gradient orientations for visual recognition and tracking. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 20-25 June 2011, Colorado Springs, USA. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5981809 |
| spellingShingle | Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja Sparse representations of image gradient orientations for visual recognition and tracking |
| title | Sparse representations of image gradient orientations for visual recognition and tracking |
| title_full | Sparse representations of image gradient orientations for visual recognition and tracking |
| title_fullStr | Sparse representations of image gradient orientations for visual recognition and tracking |
| title_full_unstemmed | Sparse representations of image gradient orientations for visual recognition and tracking |
| title_short | Sparse representations of image gradient orientations for visual recognition and tracking |
| title_sort | sparse representations of image gradient orientations for visual recognition and tracking |
| url | https://eprints.nottingham.ac.uk/31413/ https://eprints.nottingham.ac.uk/31413/ |