Unsupervised Similarity Learning through Rank Correlation and kNN Sets

Autor(es)

Valem, Lucas Pascotti ^[1] ; De Oliveira, Carlos Renan ^[1] ; Guimaraes Pedronette, Daniel Carlos ^[1] ; Almeida, Jurandy ^[2]

Afiliação do(s) autor(es)

^[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp, Av 24-A, 1515, BR-13506900 Rio Claro, SP - Brazil

^[2] Univ Fed Sao Paulo UNIFESP, Inst Ciencia & Tecnol, Av Cesare MG Lattes 1201, BR-12247014 Sao Jose Dos Campos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: ACM Transactions on Multimedia Computing Communications and Applications; v. 14, n. 4 NOV 2018.
• Citações Web of Science: 1

Resumo

The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets. (AU)

• Processo FAPESP: 16/06441-7 - Recuperação de informação semântica em grandes bases de vídeos
• Beneficiário: Jurandy Gomes de Almeida Junior
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 17/25908-6 - Aprendizado fracamente supervisionado para análise de vídeos no domínio comprimido em tarefas de recuperação e classificação para alertas visuais
• Beneficiário: João Paulo Papa
• Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 13/08645-0 - Reclassificação e agregação de listas para tarefas de recuperação de imagens
• Beneficiário: Daniel Carlos Guimarães Pedronette
• Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores
• Processo FAPESP: 17/02091-4 - Seleção e combinação de métodos de aprendizado não supervisionado para recuperação de imagens por conteúdo
• Beneficiário: Lucas Pascotti Valem
• Linha de fomento: Bolsas no Brasil - Mestrado