Outdoor Navigating Scene Labeling Using Importance Factor Based I-RELIEF and Feature Weighted Support Vector Machines

Feature selection plays an important role in terrain classification for outdoor robot navigation. For terrain classification, the image data usually have a large number of feature dimensions. The better selection of features usually results in higher labeling accuracy. In this work, a novel approach for terrain perception using Importance Factor based I-Relief algorithm and Feature Weighted Support Vector Machines (IFIR-FWSVM) is put forward. Firstly, the weight of each feature for classification is computed by using Importance Factor based I-Relief algorithm (IFIR) and the irrelevant features are eliminated. Then the weighted features are used to compute the kernel functions of SVM and trained the classifier. Finally, the trained SVM is employed to predict the terrain label in the far-field regions. Experimental results based on DARPA datasets show that the proposed method IFIR-FWSVM is superior over traditional SVM.

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Efficient Technique for Facial Image Recognition With Support Vector Machines in 2D Images With Cross-validation in Matlab

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2020.15.18 ◽

2020 ◽

Vol 15 ◽

Keyword(s):

Support Vector Machines ◽

Cross Validation ◽

Facial Recognition ◽

Kernel Functions ◽

Efficient Technique ◽

Support Vector ◽

Facial Image ◽

Vector Machines ◽

Validation Technique ◽

2D Images

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.

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A novel approach to mass detection in digital mammography based on Support Vector Machines(SVM)

Digital Mammography ◽

10.1007/978-3-642-59327-7_93 ◽

2003 ◽

pp. 399-401 ◽

Cited By ~ 3

Author(s):

Renato Campanini ◽

Armando Bazzani ◽

Alessandro Bevilacqua ◽

Dante Bollini ◽

Danilo Dongiovanni ◽

...

Keyword(s):

Support Vector Machines ◽

Digital Mammography ◽

Support Vector ◽

Mass Detection ◽

Novel Approach ◽

Vector Machines

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Classification in GIS Using Support Vector Machines

Handbook of Research on Geoinformatics ◽

10.4018/978-1-59140-995-3.ch014 ◽

2010 ◽

pp. 106-112 ◽

Cited By ~ 1

Author(s):

Alina Lazar ◽

Bradley A. Shellito

Keyword(s):

Support Vector Machines ◽

Urban Development ◽

Rural Areas ◽

Urban Areas ◽

Kernel Functions ◽

Support Vector ◽

Future Trends ◽

Error Signal ◽

Vector Machines

Support Vector Machines (SVM) are powerful tools for classification of data. This article describes the functionality of SVM including their design and operation. SVM have been shown to provide high classification accuracies and have good generalization capabilities. SVM can classify linearly separable data as well as nonlinearly separable data through the use of the kernel function. The advantages of using SVM are discussed along with the standard types of kernel functions. Furthermore, the effectiveness of applying SVM to large, spatial datasets derived from Geographic Information Systems (GIS) is also described. Future trends and applications are also discussed – the described extracted dataset contains seven independent variables related to urban development plus a class label which denotes the urban areas versus the rural areas. This large dataset, with over a million instances really proves the generalization capabilities of the SVM methods. Also, the spatial property allows experts to analyze the error signal.

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Training Support Vector Machines on Large Sets of Image Data

Computer Vision – ACCV 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-12297-2_32 ◽

2010 ◽

pp. 331-340

Author(s):

Ignas Kukenys ◽

Brendan McCane ◽

Tim Neumegen

Keyword(s):

Support Vector Machines ◽

Image Data ◽

Support Vector ◽

Training Support ◽

Large Sets ◽

Vector Machines

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A novel approach for accurate prediction of spontaneous passage of ureteral stones: Support vector machines

Kidney International ◽

10.1038/sj.ki.5000010 ◽

2006 ◽

Vol 69 (1) ◽

pp. 157-160 ◽

Cited By ~ 14

Author(s):

F. Dal Moro ◽

A. Abate ◽

G.R.G. Lanckriet ◽

G. Arandjelovic ◽

P. Gasparella ◽

...

Keyword(s):

Support Vector Machines ◽

Accurate Prediction ◽

Ureteral Stones ◽

Support Vector ◽

Spontaneous Passage ◽

Novel Approach ◽

Vector Machines

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Lymph diseases diagnosis approach based on support vector machines with different kernel functions

2014 9th International Conference on Computer Engineering & Systems (ICCES) ◽

10.1109/icces.2014.7030956 ◽

2014 ◽

Cited By ~ 3

Author(s):

Hanaa Ismail Elshazly ◽

Abeer Mohamed Elkorany ◽

Aboul Ella Hassanien

Keyword(s):

Support Vector Machines ◽

Kernel Functions ◽

Support Vector ◽

Vector Machines ◽

Diagnosis Approach

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Image Segmentation Based on Least Squares Support Vector Machines

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.4314 ◽

2014 ◽

Vol 644-650 ◽

pp. 4314-4318

Author(s):

Xin You Wang ◽

Ya Li Ning ◽

Xi Ping He

Keyword(s):

Image Segmentation ◽

Support Vector Machines ◽

Least Squares ◽

Color Image ◽

Pso Algorithm ◽

Kernel Functions ◽

Color Image Segmentation ◽

Support Vector ◽

Standard Data ◽

Vector Machines

In order to solve the problem of the conventional methods operated directly in the image, difficult to obtain good results because they are poor in high dimension performance. In this paper, a new method was proposed, which use the Least Squares Support Vector Machines in image segmentation. Furthermore, the parameters of kernel functions are also be optimized by Particle Swarm Optimization (PSO) algorithm. The practical application in various of standard data sets and color image segmentation experiment. The results show that, LS-SVM can use a variety of features in image, the experiments have achieved good results of image segmentation, and the time needed for segmentation is greatly reduced than standard SVM.

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