Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent attempts demonstrate that learning an appropriate distance metric in visual tracking applications can improve the tracking performance. Hover, the existing metric learning methods learn and adjust the distance beten all pairwise sample points in an iterative way, which raises the time consumption issue in real-time tracking applications. To address this problem, this paper proposes a novel metric learning method and applies it to visual tracking. The main idea of the proposed method is to adapt the distance from each individual sample point to a few anchor points instead of the distance beten all pairs of samples, so as to reduce the number of distances to be adjusted. Based on this idea,  construct a convex matrix function that collapses the sample points to their class centers and maximizes the inter-class distance. Given  n  training samples in  d   -dimensional space, the equation can be solved in a closed form with the computational complexity of   O(d 2 n)     O  (    d    2    n  )       . This is much more computationally efficient than traditional methods with the computational complexity of   O( dn  2 )     O  (    dn    2    )        in each iteration (normally in tracking applications,   d⪡n     d  ⪡  n       ). Furthermore, the proposed method can be learned in an online manner which is able to accelerate the learning process and improve the matching accuracy in visual tracking applications. Experiments on UCI datasets demonstrate that the proposed learning method is comparable with the traditional metric learning methods in term of classification accuracy but much more time efficient. The comparison experiments on benchmark video sequences show that the tracking algorithm based on our learning approach can outperform the state-of-the-art tracking algorithms.
['Sihua Yi', 'Nan Jiang', 'Xinggang Wang', 'Wenyu Liu']
I am writing a research paper on a idea Individual adaptive metric learning for visual tracking
[{"from": "human", "value": "I am writing a research paper on a idea Individual adaptive metric learning for visual 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 attempts demonstrate that learning an appropriate distance metric in visual tracking applications can improve the tracking performance. Hover, the existing metric learning methods learn and adjust the distance beten all pairwise sample points in an iterative way, which raises the time consumption issue in real-time tracking applications. To address this problem, this paper proposes a novel metric learning method and applies it to visual tracking. The main idea of the proposed method is to adapt the distance from each individual sample point to a few anchor points instead of the distance beten all pairs of samples, so as to reduce the number of distances to be adjusted. Based on this idea,  construct a convex matrix function that collapses the sample points to their class centers and maximizes the inter-class distance. Given  n  training samples in  d \u00a0 -dimensional space, the equation can be solved in a closed form with the computational complexity of   O(d 2 n)     O  (    d    2    n  )       . This is much more computationally efficient than traditional methods with the computational complexity of   O( dn  2 )     O  (    dn    2    )        in each iteration (normally in tracking applications,   d\u2aa1n     d  \u2aa1  n       ). Furthermore, the proposed method can be learned in an online manner which is able to accelerate the learning process and improve the matching accuracy in visual tracking applications. Experiments on UCI datasets demonstrate that the proposed learning method is comparable with the traditional metric learning methods in term of classification accuracy but much more time efficient. The comparison experiments on benchmark video sequences show that the tracking algorithm based on our learning approach can outperform the state-of-the-art tracking algorithms."}]