Publication: Unsupervised Manifold Learning for Video Genre Retrieval
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Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
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Volume Title
Publisher
Springer
Type
Work presented at event
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Acesso aberto
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.
Description
Keywords
video genre retrieval, ranking methods, manifold learning
Language
English
Citation
Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 604-612, 2014.
