| Full text | |
| Author(s): |
Almeida, Jurandy
;
Pedronette, Daniel C. G.
;
Penatti, Otavio A. B.
;
BayroCorrochano, E
;
Hancock, E
Total Authors: 5
|
| Document type: | Journal article |
| Source: | PROGRESS IN PATTERN RECOGNITION IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2014; v. 8827, p. 9-pg., 2014-01-01. |
| Abstract | |
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. (AU) | |
| 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 |