Hankel subspace method for efficient gesture representation

Gatto, Bernardo B.; Bogdanova, Anna; Souza, Lincon S.; Santos, Eulanda M. dos

doi:10.1109/mlsp.2017.8168114

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Although controlling machines employing gesture recognition is useful, it includes many difficulties; for instance, the distribution of a gesture largely varies depending on viewpoints due to its multiple joint structures. To solve these problems, we introduce the Hankel Mutual Subspace Method [19,20] based on the Hankel matrix formulation to describe pattern sets and the MSM framework, as illustrated in Figure 4. The problem formulation of matching time-aware pattern-sets is similar to the problem of pattern-set matching observed in the previous section, except that the ordering of the patterns should be preserved since some gesture classes present their semantic information correlated to the pattern ordering.…”

Section: Hankel Subspacesmentioning

confidence: 99%

Pattern-set Representations using Linear, Shallow and Tensor Subspaces

Gatto

Santos²,

Júnior³

2022

CLEIej

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Pattern-set matching refers to a class of problems where learning takes place through sets rather than elements. Much used in computer vision, this approach presents robustness to variations such as illumination, intrinsic parameters of the signal capture devices, and pose of the analyzed object. Inspired by applications of subspace analysis, three new collections of methods are presented in this paper: (1) New representations for two-dimensional sets; (2) Shallow networks for image classification; and (3) Subspaces for tensor representation and classification. New representations are proposed with the aim of preserving the spatial structure and maintaining a fast processing time. We also introduce a technique to keep temporal structure, even using the principal component analysis, which classically does not model sequences. In shallow networks, we present two convolutional neural networks that do not require backpropagation, employing only subspaces for its convolution filters. These networks present advantages when the training time and hardware resources are scarce. Finally, to handle tensor data, such as video data, we propose methods that employ subspaces for representation in a compact and discriminative way. Our proposed work has been applied to several problems, such as 2D data representation, shallow networks for image classification, and tensor representation and learning.

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Section: Hankel Subspacesmentioning

confidence: 99%

Pattern-set Representations using Linear, Shallow and Tensor Subspaces

Gatto

Santos²,

Júnior³

2022

CLEIej

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“…Some work also received international awards. A1 ICIP [14] A1 CVPRW [15], [16] A1 ICDAR [17] A2 IJCNN [18] A2 ICTAI [19] A3 SIBGRAPI [20] A3 MLSP [21], [22] A4 BRACIS [23], [24] A4 MVA [25], [26], [27] B1 A1 ASOC [28] A1 PR [29] A1 NEPL [30] A3 EURASIP JIVP [31] A4…”

Section: Awards Publications and Distinctionsmentioning

confidence: 99%

Advances in subspace learning and its applications

Gatto¹,

Fukui²,

Júnior³

et al. 2021

Anais Estendidos Da XXXIV Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2021)

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Pattern-set matching refers to a class of problems where learning takes place through sets rather than elements. Much used in computer vision, this approach presents robustness to variations such as illumination, intrinsic parameters of the signal capture devices, and pose of the analyzed object. Inspired by applications of subspace analysis, three new collections of methods are presented in this thesis summary: (1) New representations for two-dimensional sets; (2) Shallow networks for image classification; and (3) Tensor data representation by subspaces. New representations are proposed to preserve the spatial structure and maintain a fast processing time. We also introduce a technique to keep temporal structure, even using the principal component analysis, which classically does not model sequences. In shallow networks, we present two convolutional neural networks that do not require backpropagation, employing only subspaces for their convolution filters. These networks present advantages when the training time and hardware resources are scarce. Finally, to handle tensor data, such as videos, we propose methods that employ subspaces for representation in a compact and discriminative way. Our proposed work has been applied in problems other than computer vision, such as representation and classification of bioacoustics and text patterns.

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