Face Data Discriminative Feature Extraction Based on Weighted Maximum Margin Criterion

2022 ◽  
pp. 558-564
Author(s):  
Jing Liu ◽  
Tong Zhang ◽  
Yangdongbo Xu ◽  
Yi Liu
Keyword(s):  
Feature Extraction ◽  
Maximum Margin ◽  
Discriminative Feature ◽  
Maximum Margin Criterion

Feature extraction by structured stepwise nonparametric maximum margin criterion

2006 ◽  
Author(s):  
Yujie Zheng ◽  
Xiaojun Wu ◽  
Dongjun Yu ◽  
Jingyu Yang ◽  
Weidong Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Maximum Margin ◽  
Maximum Margin Criterion

Early Image Fire-Detection Based on Maximum Margin Criterion

2014 ◽  
Vol 526 ◽  
pp. 324-329
Author(s):  
Jie Yuan ◽  
Hai Bing Hu ◽  
Wei Yuan ◽  
Yang Jia ◽  
Yong Ming Zhang
Keyword(s):  
Feature Extraction ◽  
Mathematical Description ◽  
Main Idea ◽  
Fire Detection ◽  
Color Model ◽  
Scatter Matrix ◽  
Maximum Margin ◽  
Maximum Margin Criterion ◽  
Early Image ◽  
The Difference

Nowadays as camera is applied widely, image fire detection becomes much popular. Many researchers are committed to analyze the RGB color model or even gray images. Actually they have some disadvantages. So this paper will present a new model based on Maximum Margin Criterion, a feature extraction criterion. As it is maximizing the difference of between-class scatter matrices and within-class scatter matrices, it does not depend on the nonsingularity of the within-class scatter matrix. First we will introduce the main idea and then give a mathematical description to apply the model to fire detection, with the algorithm we can calculate the result we need. At last we will put them into practice, use a database to do some experiments to present the performance of this method.

scholarly journals Block-Wise Two-Dimensional Maximum Margin Criterion for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao-Zhang Liu ◽  
Guan Yang
Keyword(s):  
Feature Extraction ◽  
Matrix Multiplication ◽  
Closed Form Solution ◽  
Image Data ◽  
Form Solution ◽  
Two Dimensional ◽  
Maximum Margin ◽  
Maximum Margin Criterion ◽  
Bilateral Projection ◽  
Characteristics Of Image

Maximum margin criterion (MMC) is a well-known method for feature extraction and dimensionality reduction. However, MMC is based on vector data and fails to exploit local characteristics of image data. In this paper, we propose a two-dimensional generalized framework based on a block-wise approach for MMC, to deal with matrix representation data, that is, images. The proposed method, namely, block-wise two-dimensional maximum margin criterion (B2D-MMC), aims to find local subspace projections using unilateral matrix multiplication in each block set, such that in the subspace a block is close to those belonging to the same class but far from those belonging to different classes. B2D-MMC avoids iterations and alternations as in current bilateral projection based two-dimensional feature extraction techniques by seeking a closed form solution of one-side projection matrix for each block set. Theoretical analysis and experiments on benchmark face databases illustrate that the proposed method is effective and efficient.

Feature Extraction Using Laplacian Maximum Margin Criterion

2011 ◽  
Vol 33 (1) ◽  
pp. 99-110 ◽  
Author(s):  
Wankou Yang ◽  
Changyin Sun ◽  
Helen S. Du ◽  
Jingyu Yang
Keyword(s):  
Feature Extraction ◽  
Maximum Margin ◽  
Maximum Margin Criterion
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