Manuscript received January 5, 2026; revised January 16, 2026; accepted March 6, 2026; published July 21, 2026.

Abstract—A cross-domain and unsupervised Re-Identification (Re-ID) is primarily impacted by the challenges presented by domain shift, intra-class heterogeneity across datasets, and the lack of annotated target pictures. When training and testing take place within the same dataset, these influences are lessened in the in-domain context. In real-time, scalable systems, fine-grained part-based representations like unsupervised horizontal pyramid similarity learning struggle to capture adaptive cluster semantics and integrate generalization. Cluster Integrated Horizontal Pyramid Feature Encoding (CI-HPFE), a deep learning architecture, addresses these issues in this study. Horizontal pyramid-based feature extraction encodes high-resolution local and global body components with key improvements in our model. First, the Pyramid-based Horizontal Part Feature Encoder (PHPFE) splits spatial feature maps into multi-scale horizontal sections (levels 1, 2, 4, 8) and extracts robust, granular embeddings via stripe-wise pooling and processing. Besides feature extraction, a cluster-aware embedding module incorporates unsupervised clustering feedback into representation learning. Identity representations can change with cluster centroids. To achieve alignment and discriminability, our hybrid loss function combines local-part granularity with global identity coherence using triplet margin loss, entropy minimization, and cluster consistency loss. To reduce domain shift, Stripe encoders use a Domain-Adaptive Instance Normalization (DAIN) module to learn domain-conditioned affine statistics. For distributed deployment, a Federated Cluster Synchronization Mechanism (FCSM) synchronizes embedding spaces among edge devices without sharing raw picture data, boosting scalability, privacy, and speed. Benchmark datasets use is Market-1501 and DukeMTMC-ReID, which show considerable performance gains, including 5–6% accuracy, ~6% mAP uplift, and enhanced resilience during in-domain assessment. For homogeneous-domain person re-identification systems, the suggested architecture thus strikes a balance between methodological familiarity, unique functional modules, and deployment-level practicality.

Keywords—person Re-Identification (Re-ID), horizontal pyramid encoding, unsupervised clustering, domain adaptation, deep feature embeddings
 

Cite: Abhinav A. Parkhi and Atish S. Khobragade, "Clustering-integrated Horizontal Pyramid Feature Embedding for In-domain Unsupervised Person Re-identification," Journal of Image and Graphics, Vol. 14, No. 4, pp. 655-669, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).