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A person re-identification algorithm by exploiting region-based feature salience

机译：利用基于区域的特征显着性的人员重新识别算法

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摘要

Due to the changes of the pose and illumination, the appearances of the person captured in surveillance may have obvious variation. Different parts of persons will possess different characteristics. Applying the same feature extraction and description to all parts without differentiating their characteristics will result in poor re-identification performances. Therefore, a person re-identification algorithm is proposed to fully exploit region-based feature salience. Firstly, each person is divided into the upper part and the lower part. Correspondingly, a part-based feature extraction algorithm is proposed to adopt different features for different parts. Moreover, the features of every part are separately represented to retain their salience. Secondly, in order to accurately represent the color feature, the salient color descriptor is proposed by considering the color diversity between current region and its surrounding regions. The experimental results demonstrate that the proposed algorithm can improve the accuracy of person re-identification compared with the state-of-the-art algorithms. (C) 2015 Elsevier Inc. All rights reserved.
机译：由于姿势和照明的变化，监视中捕获的人的外观可能会有明显的变化。人的不同部位将具有不同的特征。将相同的特征提取和描述应用于所有零件而未区分其特征将导致较差的重新识别性能。因此，提出了一种人员重新识别算法，以充分利用基于区域的特征显着性。首先，每个人都分为上半部分和下半部分。相应地，提出了一种基于零件的特征提取算法，以针对不同零件采用不同的特征。而且，每个部分的特征都被分别表示以保持其显着性。其次，为了准确地表示颜色特征，通过考虑当前区域及其周围区域之间的颜色多样性，提出了显着的颜色描述符。实验结果表明，与最新算法相比，该算法可以提高人员重新识别的准确性。 （C）2015 Elsevier Inc.保留所有权利。

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