scholarly journals A Fuzzy Kernel Maximum Margin Criterion for Image Feature Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Shibin Xuan
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Training Sample ◽  
Computational Method ◽  
Image Feature ◽  
Kernel Principal Component Analysis ◽  
Image Feature Extraction ◽  
Scatter Matrix ◽  
Maximum Margin ◽  
Maximum Margin Criterion

Based on kernel principal component analysis, fuzzy set theory, and maximum margin criterion, a novel image feature extraction and recognition method, called fuzzy kernel maximum margin criterion (FKMMC), is proposed. In the proposed method, two new fuzzy scatter matrixes are redefined. The new fuzzy scatter matrix can reflect fully the relation between fuzzy membership degree and the offset of the training sample to subclass center. Besides, a concise reliable computational method of the fuzzy between-class scatter matrix is provided. Experimental results on four face databases (AR, extended Yale B, GTFD, and FERET) demonstrate that the proposed method outperforms other methods.

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 Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Sen Jia ◽  
Zhangwei Zhan ◽  
Meng Xu
Keyword(s):  
Feature Extraction ◽  
Adaptive Filters ◽  
Multiple Scales ◽  
Principal Component ◽  
Training Sample ◽  
Extraction Methods ◽  
Kernel Principal Component Analysis ◽  
Lidar Data ◽  
Filter Size ◽  
Real World Datasets

The joint interpretation of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data has developed rapidly in recent years due to continuously evolving image processing technology. Nowadays, most feature extraction methods are carried out by convolving the raw data with fixed-size filters, whereas the structural and texture information of objects in multiple scales cannot be sufficiently exploited. In this article, a shearlet-based structure-aware filtering approach, abbreviated as ShearSAF, is proposed for HSI and LiDAR feature extraction and classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is firstly adopted on raw HSIs to reduce the dimensions. Then, the KPCA-reduced HSI and LiDAR data are converted to the shearlet domain for texture and area feature extraction. In contrast, superpixel segmentation algorithm utilizes the raw HSI data to obtain the initial oversegmentation map. Subsequently, by utilizing a well-designed minimum merging cost that fully considers spectral (HSI and LiDAR data), texture, and area features, a region merging procedure is gradually conducted to produce a final merging map. Further, a scale map that locally indicates the filter size is achieved by calculating the edge distance. Finally, the KPCA-reduced HSI and LiDAR data are convolved with the locally adaptive filters for feature extraction, and a random forest (RF) classifier is thus adopted for classification. The effectiveness of our ShearSAF approach is verified on three real-world datasets, and the results show that the performance of ShearSAF can achieve an accuracy higher than that of comparison methods when exploiting small-size training sample problems. The codes of this work will be available at http://jiasen.tech/papers/ for the sake of reproducibility.

Sign in / Sign up

Export Citation Format

Share Document