Autor(es)

Kawai, Vinicius Atsushi Sato ; Leticio, Gustavo Rosseto ; Valem, Lucas Pascotti ; Pedronette, Daniel Carlos Guimardes

• Tipo de documento: Artigo Científico
• Fonte: 2024 37TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES, SIBGRAPI 2024; v. N/A, p. 6-pg., 2024-01-01.

Resumo

Despite the impressive advances in image understanding approaches, defining similarity among images remains a challenging task, crucial for many applications such as classification and retrieval. Mainly supported by Convolution Neural Networks (CNNs) and Transformer-based models, image representation techniques are the main reason for the advances. On the other hand, comparisons are mostly computed based on traditional pairwise measures, such as the Euclidean distance, while contextual similarity approaches can lead to effective results in defining similarity between points in high-dimensional spaces. This paper introduces a novel approach to contextual similarity by combining two techniques: neighbor embedding projection methods and rank-based manifold learning. High-dimensional features are projected in a 2D space used for efficiently ranking computation. Subsequently, manifold learning methods are exploited for a re-ranking step. An experimental evaluation conducted on different datasets and visual features indicates that the proposed approach leads to significant gains in comparison to the original feature representations and the neighbor embedding method in isolation. (AU)

• Processo FAPESP: 18/15597-6 - Aplicação e investigação de métodos de aprendizado não-supervisionado em tarefas de recuperação e classificação
• Beneficiário: Daniel Carlos Guimarães Pedronette
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2