Unsupervised Effectiveness Estimation Through Intersection of Ranking References

Autor(es)

Camacho Presotto, Joao Gabriel ; Valem, Lucas Pascotti ; Pedronette, Daniel Carlos Guimaraes ; Vento, M ; Percannella, G

• Tipo de documento: Artigo Científico
• Fonte: COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT II; v. 11679, p. 14-pg., 2019-01-01.

Resumo

Estimating the effectiveness of retrieval systems in unsupervised scenarios consists in a task of crucial relevance. By exploiting estimations which dot not require supervision, the retrieval results of many applications as rank aggregation and relevance feedback can be improved. In this paper, a novel approach for unsupervised effectiveness estimation is proposed based the intersection of ranking references at top-k positions of ranked lists. An experimental evaluationwas conducted considering public datasets and different image features. The linear correlation between the proposed measure and the effectiveness evaluation measures was assessed, achieving high scores. In addition, the proposed measure was also evaluated jointly with rank aggregationmethods, by assigning weights to ranked lists according to the effectiveness estimation of each feature. (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
• 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
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 17/02091-4 - Seleção e combinação de métodos de aprendizado não supervisionado para recuperação de imagens por conteúdo
• Beneficiário: Lucas Pascotti Valem
• Modalidade de apoio: Bolsas no Brasil - Mestrado