Actas de congresos
A rank aggregation framework for video interestingness prediction
Fecha
2017-01-01Registro en:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10484 LNCS, p. 3-14.
1611-3349
0302-9743
10.1007/978-3-319-68560-1_1
2-s2.0-85032487714
Autor
Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
Institución
Resumen
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.