Secondly, LBP and gray level co-occurrence matrix features were combined.
The combination of histogram features and gray level gradient co-occurrence matrix features is suggested for good diagnosis accuracy and low time cost.
In this study, co-occurrence matrix features were significantly different between ILC and IDC, allowing differentiation between these two histological subtypes, and were superior to the other texture methods applied including histogram analysis, run-length matrix, autoregressive model, and wavelet transform [22].
Most of these block-in-matrix features are too small to be seen in this photo.
In our paper, we employ the texture feature information of auxiliary data set to build the similarity matrix for target data set, and then by exploiting the texture information structure of the similarity matrix, the valuable features are mapped to the spectral space and the textural space.
Gray-level co-occurrence matrix based texture features are extracted from two-level decomposition of wavelet coefficients of cervix regions extracted from CECT images.
Concurrence matrix-based texture features are incorporated into the feature vector to further improve the texture classification sensitivity.
The linear prediction matrices for the four features are combined to find the same set of linear prediction coefficients, from which we estimate the spectrum of the DNA sequence and detect exons based on the 1/3 frequency component.
Based on the time frequency matrix of MST, four disturbance features are enough to construct the feature vector, solving the problem of the statistical feature redundancy.
In this approach multivariate, low-cost color features are extracted to construct a feature matrix which is then processed using sliding window singular value decomposition.
The original features are projected into a new feature space ({mathcal{B}}) with a mapping function ϕ, and the new feature matrix is generated and written by: F = left( {begin{array}{*{20}c} {varPhi (x_{1} )} {varPhi (x_{2} )} vdots {varPhi (x_{n} )} end{array} } right) (2).
