Static Video Summarization through Optimum-Path Forest Clustering

Martins, Guilherme Brandão; Afonso, Luis Cláudio Súgi; Osaku, Daniel; Almeida, Jurandy; Papa, João Paulo

doi:10.1007/978-3-319-12568-8_108

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2015

2023

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Cited by 5 publications

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“…Later on, Osaku et al 19 presented a contextual-based OPF to classify remote sensing images, and Pereira et al 20 introduced a sequential learning approach for the same context using supervised OPF. A multi-label version of the OPF classifier was applied for video classification by Pereira et al, 21 as well as an OPF-based video summarization approach was proposed by Martins et al 22 …”

Section: 17mentioning

confidence: 99%

Recent Advances on Optimum-Path Forest for Data Classification: Supervised, Semi-Supervised, and Unsupervised Learning

Papa

Amorim²,

Falcão

et al. 2015

Handbook of Pattern Recognition and Computer Vision

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Although one can find several pattern recognition techniques out there, there is still room for improvements and new approaches. In this book chapter, we revisited the Optimum-Path Forest (OPF) classifier, which has been evaluated over the last years in a number of applications that consider supervised, semi-supervised and unsupervised learning problems. We also presented a brief compilation of a number of previous works that employed OPF in different research fields, that range from remote sensing image classification to medical data analysis.

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