Contextual Similarity Learning for Image Retrieval and Classification: Applications in Person Re-Identification

The rapid expansion of image collections has driven the need for advanced machine learning and image retrieval applications. However, many existing methods depend on large labeled datasets for training, which are costly. To address this, various techniques have been developed, with a key challenge being the effective definition of image similarity. Most approaches still rely on pairwise similarity measures, overlooking valuable contextual information. This work enhances image retrieval and classification by leveraging contextual similarity, proposing seven novel methods applied to general-purpose and person reidentification (Re-ID) datasets. Extensive experiments show that the proposed methods perform comparably to or better than state-of-the-art approaches.