Unsupervised manifold learning for video genre retrieval

Almeida, Jurandy; Pedronette, Daniel C.G. [UNESP]; Penatti, Otávio A.B.

Unsupervised manifold learning for video genre retrieval

Data

2014-01-01

Autores

Almeida, Jurandy

Pedronette, Daniel C.G.

Penatti, Otávio A.B.

Tipo

Trabalho apresentado em evento

Direito de acesso

Acesso aberto

Resumo

This paper investigates the perspective of exploiting pairwise similarities to improve the performance of visual features for video genre retrieval. We employ manifold learning based on the reciprocal neighborhood and on the authority of ranked lists to improve the retrieval of videos considering their genre. A comparative analysis of different visual features is conducted and discussed. We experimentally show in the dataset of 14,838 videos from the MediaEval benchmark that we can achieve considerable improvements in results. In addition, we also evaluate how the late fusion of different visual features using the same manifold learning scheme can improve the retrieval results.

Palavras-chave

Manifold learning, Ranking methods, Video genre retrieval

Idioma

Inglês

Citação

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 604-612.