A Multi-level Weighted Representation for Person Re-identification

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The introduction of deep neural networks (DNN) into person re-identification tasks has significantly improved the re-identification accuracy. However, the substantial characteristics of features extracted from different layers of convolutional neural networks (CNN) are infrequently considered in existing methods. In this paper, we propose a multi-level weighted representation for person re-identification, in which features containing strong discriminative powers or rich semantic meanings are extracted from different layers of a deep CNN, and an estimation subnet evaluates the quality of each feature and generates quality scores used as concatenation weights for all multi-level features. The features multiplied by their weights are concatenated together to the final representations which are improved eventually by a triplet loss to increase the inter-class distance. Therefore, the representation exploits the various benefits of different level features jointly. Experiments on the iLIDS-VID and PRID 2011 datasets show that our proposed representation significantly outperforms the baseline and the state of the art methods.