Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 porful image-based object recognition schemes. ,  propose a framework for visual recognition and tracking based on sparse representations of image gradient orientations.  show that minimal l 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].  further show that low-dimensional embeddings generated from gradient orientations perform equally ll even when probe objects are corrupted by outliers, which, in turn, results in huge computational savings.  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,  also propose a robust and efficient l 1 -based “tracking by detection” algorithm.  show experimentally that our tracker outperforms a recently proposed l 1 -based tracking algorithm in terms of robustness, accuracy and speed.
['Georgios Tzimiropoulos', 'Stefanos Zafeiriou', 'Maja Pantic']
I am writing a research paper on a idea Sparse representations of image gradient orientations for visual recognition and tracking
[{"from": "human", "value": "I am writing a research paper on a idea Sparse representations of image gradient orientations for visual recognition and tracking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 porful image-based object recognition schemes. ,  propose a framework for visual recognition and tracking based on sparse representations of image gradient orientations.  show that minimal l 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].  further show that low-dimensional embeddings generated from gradient orientations perform equally ll even when probe objects are corrupted by outliers, which, in turn, results in huge computational savings.  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,  also propose a robust and efficient l 1 -based \u201ctracking by detection\u201d algorithm.  show experimentally that our tracker outperforms a recently proposed l 1 -based tracking algorithm in terms of robustness, accuracy and speed."}]