Let (mathbf {x}in mathbb {R}^{n}) denote the HR image patches of size (sqrt {n}times sqrt {n}), and (mathbf {y}in mathbb {R}^{m}) denote the features of LR image patches.
where v i denotes the feature vector of the i t h training sample and the permittivity ε ri is the corresponding target value.
We denote the deep features of the RGB image as a corresponding K layered rich representation (Phi ={phi ^{i}_{mathcal {M}_{s}, D_{s}}, forall i in, [!1 cdots K]}).
Naturalism is the mistake of thinking value terms like "good" straightforwardly denote the features in virtue of which we commend them.
Let f 1, f 2, ⋯, f M denote the corresponding feature vectors, where f ri denotes the rth feature of x i, where r = 1, 2, ⋯, M.
Figure 3 The bits extraction framework based on two-dimensional quantization and coding, where D denotes the number of features; K denotes the number of feature pairs; c k denotes the feature index for the kth feature pair (k=1,…,K); s k denotes the corresponding quantized bits.
(2) Extract the spectrogram feature Y test ( c, m ) c = 1, m = 1 C, M c of the slide-correlated HRRP testing sample xtest, where Y test ( c, m ) = { [ y test ( c, m ) ( 1 ), y test ( c, m ) ( 2 ), …, y test ( c, m ) ( L y ) ] } denotes the spectrogram feature of HRRP testing sample correlated with m th frame of target c as defined in (2).
Denote the feature matrix of target data set as (textbf {X}^{(t)}in mathbb {R}^{ktimes n^{(t)}}), the feature matrix of spectral information of auxiliary data set as (textbf {X}^{(a)}in mathbb {R}^{ktimes n^{(a)}}), and the texture feature information matrix in auxiliary data set as (textbf {T}^{(a)}in mathbb {R}^{mtimes n^{(a)}}).
Let F denote the feature vector of a focal modification pattern with k elements.
and denote the feature sequence matrices of two signals, where and are the number of frames in signal and, respectively.
