Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics. Hover, most existing approaches focus on learning a fixed distance metric for all instance pairs, while ignoring the individuality of each person. ,  formulate the person re-identification problem as an imbalanced classification problem and learn a classifier specifically for each pedestrian such that the matching model is highly tuned to the individual's appearance. To establish correspondence beten feature space and classifier space,  propose a Least Square Semi-Coupled Dictionary Learning (LSSCDL) algorithm to learn a pair of dictionaries and a mapping function efficiently. Extensive experiments on a series of challenging databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches, especially on the rank-1 recognition rate.
['Ying Zhang', 'Baohua Li', 'Huchuan Lu', 'Atshushi Irie', 'Xiang Ruan']
I am writing a research paper on a idea Sample-Specific SVM Learning for Person Re-identification
[{"from": "human", "value": "I am writing a research paper on a idea Sample-Specific SVM Learning for Person Re-identification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics. Hover, most existing approaches focus on learning a fixed distance metric for all instance pairs, while ignoring the individuality of each person. ,  formulate the person re-identification problem as an imbalanced classification problem and learn a classifier specifically for each pedestrian such that the matching model is highly tuned to the individual's appearance. To establish correspondence beten feature space and classifier space,  propose a Least Square Semi-Coupled Dictionary Learning (LSSCDL) algorithm to learn a pair of dictionaries and a mapping function efficiently. Extensive experiments on a series of challenging databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches, especially on the rank-1 recognition rate."}]