Image Retrieval Methods In The Dissimilarity Space

Abstract

Image retrieval methods rely on metric learning to train backbone feature extraction models that can extract discriminant queries and reference (gallery) feature representations for similarity matching. Although state-of-the-art accuracy has improved considerably with the advent of deep learning (DL) models trained on large datasets, image retrieval remains challenging in many real-world video analytics and surveillance applications, e.g., person re-identification. Using the Euclidean space for matching limits the performance in real-world applications due to the curse of dimensionality, overfitting, and sensitivity to noisy data. We argue that the feature dissimilarity space is more suitable for similarity matching, and propose a dichotomy transformation to project query and reference embeddings into a single embedding in the dissimilarity space. We also advocate for end-to-end training of a backbone and binary classification models for pair-wise matching. As opposed to comparing

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykiran2024image

Related papers

• Differential Geometric Retrieval Of Deep Features (2017)2.26
• Learning Non-metric Visual Similarity For Image Retrieval (2017)11.58
• Deep Metric Learning Using Similarities From Nonlinear Rank Approximations (2019)2.26
• Directional Statistics-based Deep Metric Learning For Image Classification And Retrieval (2018)13.05
• The Devil Is In The Middle: Exploiting Mid-level Representations For Cross-domain Instance Matching (2017)0.00
• Cross-domain Visual Matching Via Generalized Similarity Measure And Feature Learning (2016)15.54
• On The Unreasonable Effectiveness Of Centroids In Image Retrieval (2021)14.80
• Large Margin Learning In Set To Set Similarity Comparison For Person Re-identification (2017)12.10