Re-ranking Person Re-identification with k-reciprocal Encoding

Abstract

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method.

arXiv commentTo appear in CVPR 2017

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Cited by in corpus (17)

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• Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification
• The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
• Learning to Disentangle Scenes for Person Re-identification
• Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models
• Let Features Decide for Themselves: Feature Mask Network for Person Re-identification
• Person Re-identification in the Wild
• CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification
• Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
• Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification
• Adaptively Connected Neural Networks
• Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark
• Towards Visually Explaining Similarity Models
• A framework with updateable joint images re-ranking for Person Re-identification
• Person Re-identification with Bias-controlled Adversarial Training
• Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification