Similarity-based Outlier Detection for Noisy Object Re-Identification Using Beta Mixtures

• URL: http://arxiv.org/abs/2509.08926v3
• Date: Mon, 15 Sep 2025 11:52:51 GMT
• Title: Similarity-based Outlier Detection for Noisy Object Re-Identification Using Beta Mixtures
• Authors: Waqar Ahmad, Evan Murphy, Vladimir A. Krylov,
• Abstract summary: Object re-identification (Re-ID) methods are highly sensitive to label noise, which typically leads to significant performance degradation.<n>We address this challenge by reframing Re-ID as a supervised image similarity task and adopting a Siamese network architecture trained to capture discriminative pairwise relationships.<n>We demonstrate the effectiveness of Beta-SOD in de-noising and Re-ID tasks for person Re-ID, on CUHK03 and Market-1501 datasets, and vehicle Re-ID, on VeRi-776 dataset.
• Score: 0.9321708436387365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object re-identification (Re-ID) methods are highly sensitive to label noise, which typically leads to significant performance degradation. We address this challenge by reframing Re-ID as a supervised image similarity task and adopting a Siamese network architecture trained to capture discriminative pairwise relationships. Central to our approach is a novel statistical outlier detection (OD) framework, termed Beta-SOD (Beta mixture Similarity-based Outlier Detection), which models the distribution of cosine similarities between embedding pairs using a two-component Beta distribution mixture model. We establish a novel identifiability result for mixtures of two Beta distributions, ensuring that our learning task is well-posed. The proposed OD step complements the Re-ID architecture combining binary cross-entropy, contrastive, and cosine embedding losses that jointly optimize feature-level similarity learning. We demonstrate the effectiveness of Beta-SOD in de-noising and Re-ID tasks for person Re-ID, on CUHK03 and Market-1501 datasets, and vehicle Re-ID, on VeRi-776 dataset. Our method shows superior performance compared to the state-of-the-art methods across various noise levels (10-30\%), demonstrating both robustness and broad applicability in noisy Re-ID scenarios. The implementation of Beta-SOD is available at: github.com/waqar3411/Beta-SOD

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