Temporal-stochastic tensor features for action recognition

Batalo, Bojan; Souza, Lincon S.; Gatto, Bernardo B.; Sogi, Naoya; Fukui, Kiichi

doi:10.1016/j.mlwa.2022.100407

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Finally, it is worth mentioning that some deep learning approaches are being considered for HU (which still suffer from the increasing and flexible dimensionality of HSIs and the difficulty of finding data sets for training especially in a blind framework). However, by developing our methodological study of tensor-based unmixing and pushing for interpretability, this framework can help interpretability in data driven methods based on tensor decomposition [21], [59], [60].…”

Section: Discussionmentioning

confidence: 99%

MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition

Jouni,

Mura,

Drumetz

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Hyperspectral unmixing allows to represent mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image is represented as a high-order tensor with additional features in a multimodal, multifeature framework. Tensor models such as Canonical polyadic decomposition allow for this kind of unmixing, but lack a general framework and interpretability of the results. In this paper, we propose an interpretable methodological framework for low-rank Multi-feature hyperspectral unmixing based on tensor decomposition (MultiHU-TD) which incorporates the abundance sum-to-one constraint in the Alternating optimization ADMM algorithm, and provide in-depth mathematical, physical and graphical interpretation and connections with the extended linear mixing model. As additional features, we propose to incorporate mathematical morphology and reframe a previous work on neighborhood patches within MultiHU-TD. Experiments on real hyperspectral images showcase the interpretability of the model and the analysis of the results. Python and MATLAB implementations are made available on GitHub.

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Section: Discussionmentioning

confidence: 99%