Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Distance learning (DL) is an effective technique for person reidentification (PR-ID). DL based methods learn the distance metric by exploiting the discriminative information contained in samples. In PR-ID, different types of negative samples own different amounts of discriminative information, and impostor samples usually own more than other ll separable negative samples (WSN-samples). Therefore, how to make full use of the different discriminative information conveyed by all negative samples in the DL process is a critical issue to be investigated. ,  propose a novel DL approach for PR-ID. Specifically, for each target sample,  divide its negative samples into impostors and WSN-samples. Then  learn the distance metric by utilizing impostors and WSN-samples differently. For impostors,  design a symmetric triplet constraint, which requires the impostor to be far away from both samples of its corresponding positive sample pair simultaneously; for WSN-samples,  require them to keep their favorable separability. Experimental results on three benchmark datasets demonstrate the effectiveness and efficiency of our approach.
['Xiaoke Zhu', 'Xiao-Yuan Jing', 'Fei Wu', 'Wei-Shi Zheng', 'Ruimin Hu', 'Chunxia Xiao', 'Chao Liang']
I am writing a research paper on a idea Distance learning by treating negative samples differently and exploiting impostors with symmetric triplet constraint for person re-identification
[{"from": "human", "value": "I am writing a research paper on a idea Distance learning by treating negative samples differently and exploiting impostors with symmetric triplet constraint 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: Distance learning (DL) is an effective technique for person reidentification (PR-ID). DL based methods learn the distance metric by exploiting the discriminative information contained in samples. In PR-ID, different types of negative samples own different amounts of discriminative information, and impostor samples usually own more than other ll separable negative samples (WSN-samples). Therefore, how to make full use of the different discriminative information conveyed by all negative samples in the DL process is a critical issue to be investigated. ,  propose a novel DL approach for PR-ID. Specifically, for each target sample,  divide its negative samples into impostors and WSN-samples. Then  learn the distance metric by utilizing impostors and WSN-samples differently. For impostors,  design a symmetric triplet constraint, which requires the impostor to be far away from both samples of its corresponding positive sample pair simultaneously; for WSN-samples,  require them to keep their favorable separability. Experimental results on three benchmark datasets demonstrate the effectiveness and efficiency of our approach."}]