Unsupervised manifold learning for video genre retrieval
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Data
2014-01-01
Autores
Almeida, Jurandy
Pedronette, Daniel C.G. [UNESP]
Penatti, Otávio A.B.
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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.
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Manifold learning, Ranking methods, Video genre retrieval
Como citar
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 604-612.