A New Discriminative Sparse Representation Method for Robust Face Recognition via Regularization
Sparse representation has shown an attractive performance in a number of applications.
However, the available sparse representation methods still suffer from some problems, and …
However, the available sparse representation methods still suffer from some problems, and …
Fast low-rank shared dictionary learning for image classification
Despite the fact that different objects possess distinct class-specific features, they also
usually share common patterns. This observation has been exploited partially in a recently …
usually share common patterns. This observation has been exploited partially in a recently …
Rifd-cnn: Rotation-invariant and fisher discriminative convolutional neural networks for object detection
Thanks to the powerful feature representations obtained through deep convolutional neural
network (CNN), the performance of object detection has recently been substantially boosted …
network (CNN), the performance of object detection has recently been substantially boosted …
Deep dictionary learning
Two popular representation learning paradigms are dictionary learning and deep learning.
While dictionary learning focuses on learning “basis” and “features” by matrix factorization …
While dictionary learning focuses on learning “basis” and “features” by matrix factorization …
Time-series classification methods: Review and applications to power systems data
Chapter Overview The diffusion in power systems of distributed renewable energy
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
A dictionary learning approach for classification: Separating the particularity and the commonality
Empirically, we find that, despite the class-specific features owned by the objects appearing
in the images, the objects from different categories usually share some common patterns …
in the images, the objects from different categories usually share some common patterns …
Rotation awareness based self-supervised learning for SAR target recognition with limited training samples
Z Wen, Z Liu, S Zhang, Q Pan - IEEE Transactions on Image …, 2021 - ieeexplore.ieee.org
The scattering signatures of a synthetic aperture radar (SAR) target image will be highly
sensitive to different azimuth angles/poses, which aggravates the demand for training …
sensitive to different azimuth angles/poses, which aggravates the demand for training …
Sparse, collaborative, or nonnegative representation: which helps pattern classification?
The use of sparse representation (SR) and collaborative representation (CR) for pattern
classification has been widely studied in tasks such as face recognition and object …
classification has been widely studied in tasks such as face recognition and object …
Group sparsity and geometry constrained dictionary learning for action recognition from depth maps
Human action recognition based on the depth information provided by commodity depth
sensors is an important yet challenging task. The noisy depth maps, different lengths of …
sensors is an important yet challenging task. The noisy depth maps, different lengths of …
Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding
Automatic detection of geospatial targets in cluttered scenes is a profound challenge in the
field of aerial and satellite image analysis. In this paper, we propose a novel practical …
field of aerial and satellite image analysis. In this paper, we propose a novel practical …