| Texto completo | |
| Autor(es): |
Kuncheva, Ludmila I.
;
Yousefi, Paria
;
Almeida, Jurandy
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF THE 2017 SEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA 2017); v. N/A, p. 6-pg., 2017-01-01. |
| Resumo | |
Evaluation of keyframe video summaries is a notoriously difficult problem. So far, there is no consensus on guidelines, protocols, benchmarks and baseline models. This study contributes in three ways: (1) We propose a new baseline model for creating a keyframe summary, called Closest-to-Centroid, and show that it is a better contestant compared to the two most popular baselines: uniform sampling and choosing the mid-event frame. (2) We also propose a method for matching the visual appearance of keyframes, suitable for comparing summaries of egocentric videos and lifelogging photostreams. (3) We examine 24 image feature spaces (different descriptors) including colour, texture, shape, motion and a feature space extracted by a pre trained convolutional neural network (CNN). Our results using the four egocentric videos in the UTE database favour low-level shape and colour feature spaces for use with CC. (AU) | |
| Processo FAPESP: | 16/06441-7 - Recuperação de informação semântica em grandes bases de vídeos |
| Beneficiário: | Jurandy Gomes de Almeida Junior |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |