A rank aggregation framework for video interestingness prediction

Almeida, Jurandy; Valem, Lucas P. [UNESP]; Pedronette, Daniel C. G. [UNESP]

A rank aggregation framework for video interestingness prediction

Data

2017-01-01

Autores

Almeida, Jurandy

Valem, Lucas P.

Pedronette, Daniel C. G.

Tipo

Trabalho apresentado em evento

Direito de acesso

Acesso aberto

Resumo

Often, different segments of a video may be more or less attractive for people depending on their experience in watching it. Due to this subjectiveness, the challenging task of automatically predicting whether a video segment is interesting or not has attracted a lot of attention. Current solutions are usually based on learning models trained with features from different modalities. In this paper, we propose a late fusion with rank aggregation methods for combining ranking models learned with features of different modalities and by different learning-to-rank algorithms. The experimental evaluation was conducted on a benchmarking dataset provided for the Predicting Media Interestingness Task at the MediaEval 2016. Two different modalities and four learning-to-rank algorithms are considered. The results are promising and show that the rank aggregation methods can be used to improve the overall performance, reaching gains of more than 10% over state-of-the-art solutions.

Palavras-chave

Learning-to-rank methods, Multimedia information retrieval, Multimodal late fusion, Predicting media interestingness, Rank aggregation

Idioma

Inglês

Como citar

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10484 LNCS, p. 3-14.