Sparse representations of image gradient orientations for visual recognition and tracking

Tzimiropoulos, Georgios and Zafeiriou, S. and Pantic, M. (2011) Sparse representations of image gradient orientations for visual recognition and tracking. In: Conference of 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011, 20-25 June 2011, Colorado Springs.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

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 IEEE.

Additional Information:Conference Code: 87052
Keywords:Baseline methods, Block processing, Computational savings, Detection algorithm, Embeddings, Gradient orientations, Image gradients, Image-based objects, Linear representation, Over-complete, Query object, Recognition rates, Robust object recognition, Sparse representation, Tracking algorithm, Training sample, Visual recognition, Algorithms, Computer vision, Probes, Object recognition
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:8729
Deposited On:07 Apr 2013 20:22

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