| Full text | |
| Author(s): |
Almeida, Jurandy
;
Valem, Lucas P.
;
Pedronette, Daniel C. G.
;
Battiato, S
;
Gallo, G
;
Schettini, R
;
Stanco, F
Total Authors: 7
|
| Document type: | Journal article |
| Source: | IMAGE ANALYSIS AND PROCESSING,(ICIAP 2017), PT I; v. 10484, p. 12-pg., 2017-01-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 16/06441-7 - Semantic information retrieval in large video databases |
| Grantee: | Jurandy Gomes de Almeida Junior |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 17/02091-4 - Selection and combination of unsupervised learning Methdos for content-based image retrieval |
| Grantee: | Lucas Pascotti Valem |
| Support Opportunities: | Scholarships in Brazil - Master |