Shape Classification Using Hilbert Space Embeddings and Kernel Adaptive Filtering

Blandon, J. S.; Valencia, Cristhian K.; Alvarez, Azucena L.; Echeverry, Julian David; Álvarez, Mauricio A.; Orozco, Álvaro A.

doi:10.1007/978-3-319-93000-8_28

Cited by 2 publications

(1 citation statement)

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“…In the case of AFs, they allow recursive learning of the time series, giving prominence to the most relevant data samples [ 29 ]. However, the quantization size and the error tolerance must be tuned appropriately, which can be problematic for 3D skeletal-based samples [ 30 ]. Regarding the GP-based methods, a Bayesian representation of time series is carried out.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

Valencia-Marin

Pulgarín-Giraldo

Velásquez-Martínez

et al. 2021

Sensors

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Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class).

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Section: Introductionmentioning

confidence: 99%