A Multiple Labeling-Based Optimum-Path Forest for Video Content Classification

Pereira, Luiz Haroldo; Papa, João Paulo; Almeida, Jurandy; Torres, Ricardo da S.; Amorim, Willian Paraguassu

doi:10.1109/sibgrapi.2013.53

Cited by 3 publications

(2 citation statements)

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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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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

Self Cite

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“…The most interesting semi-supervised learning methods explore the distribution of supervised and unsupervised training samples in the feature space for label propagation purposes [1,2,4,5,[17][18][19]28] . Others use path-based similarity to capture the structure of the data and maximize the separability among classes [7,35] .…”

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Multi-label semi-supervised classification through optimum-path forest

Amorim

Falcão

Papa

2018

Information Sciences

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a b s t r a c tMulti-label classification consists of assigning one or multiple classes to each sample in a given dataset. However, the project of a multi-label classifier is usually limited to a small number of supervised samples as compared to the number of all possible label combinations. This scenario favors semi-supervised learning methods, which can cope with the absence of supervised samples by adding unsupervised ones to the training set. Recently, we proposed a semi-supervised learning method based on optimum connectivity for singlelabel classification. In this work, we extend it for multi-label classification with considerable effectiveness gain. After a single-label data transformation, the method propagates labels from supervised to unsupervised samples, as in the original approach, by assuming that samples from the same class are more closely connected through sequences of nearby samples than samples from distinct classes. Given that the procedure is more reliable in high-density regions of the feature space, an additional step repropagates labels from the maxima of a probability density function to correct possible labeling errors from the previous step. Finally, the data transformation is reversed to obtain multiple labels per sample. The new approach is experimentally validated on several datasets in comparison with state-of-the-art methods.

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Object-Based Video Multi-Label Classification with an Improved 3D Convolutional Neural Network

Zhou

Jiao

et al. 2019

2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA)

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A Multiple Labeling-Based Optimum-Path Forest for Video Content Classification

Cited by 3 publications

References 18 publications

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

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

Multi-label semi-supervised classification through optimum-path forest

Object-Based Video Multi-Label Classification with an Improved 3D Convolutional Neural Network

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