# Deep Constrained Dominant Sets for Person Re-identification

## TL;DR

This paper introduces a novel end-to-end constrained clustering approach called deep constrained dominant sets (DCDS) for person re-identification, leveraging probe constraints and multi-scale features to improve accuracy over existing methods.

## Contribution

The paper proposes a new constrained clustering framework for person re-id that incorporates probe constraints and end-to-end optimization, enhancing robustness and performance.

## Key findings

- Outperforms state-of-the-art methods on benchmark datasets.
- Effectively leverages probe constraints to reduce noise propagation.
- Integrates multi-scale ResNet features for improved accuracy.

## Abstract

In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (re-id) problem. Deep neural networks (DNN) have recently proven to be effective on person re-identification task. In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity. However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process. To overcome this, we propose an intriguing scheme which treats person-image retrieval problem as a {\em constrained clustering optimization} problem, called deep constrained dominant sets (DCDS). Given a probe and gallery images, we re-formulate person re-id problem as finding a constrained cluster, where the probe image is taken as a constraint (seed) and each cluster corresponds to a set of images corresponding to the same person. By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images. We further enhance the performance by integrating an auxiliary net alongside DCDS, which employs a multi-scale Resnet. To validate the effectiveness of our method we present experiments on several benchmark datasets and show that the proposed method can outperform state-of-the-art methods.