Vector quantization and multi class support vector machines based fingerprint classification

2016 ◽  
Author(s):  
Sabarna Choudhury ◽  
Shreyasi Bandyopadhyay ◽  
Sayan Mukhopadhyay ◽  
Subhajit Mukherjee
Keyword(s):  
Support Vector Machines ◽  
Vector Quantization ◽  
Support Vector ◽  
Fingerprint Classification ◽  
Vector Machines

scholarly journals Classification of Incidental Carcinoma of the Prostate Using Learning Vector Quantization and Support Vector Machines

2004 ◽  
Vol 26 (1-2) ◽  
pp. 45-55
Author(s):  
Torsten Mattfeldt ◽  
Danilo Trijic ◽  
Hans‐Werner Gottfried ◽  
Hans A. Kestler
Keyword(s):  
Support Vector Machines ◽  
Gleason Score ◽  
Vector Quantization ◽  
Volume Fraction ◽  
Learning Vector Quantization ◽  
Support Vector ◽  
Stepwise Logistic Regression ◽  
P53 Overexpression ◽  
Vector Machines ◽  
Input Variables

The subclassification of incidental prostatic carcinoma into the categories T1a and T1b is of major prognostic and therapeutic relevance. In this paper an attempt was made to find out which properties mainly predispose to these two tumor categories, and whether it is possible to predict the category from a battery of clinical and histopathological variables using newer methods of multivariate data analysis. The incidental prostatic carcinomas of the decade 1990–99 diagnosed at our department were reexamined. Besides acquisition of routine clinical and pathological data, the tumours were scored by immunohistochemistry for proliferative activity and p53‐overexpression. Tumour vascularization (angiogenesis) and epithelial texture were investigated by quantitative stereology. Learning vector quantization (LVQ) and support vector machines (SVM) were used for the purpose of prediction of tumour category from a set of 10 input variables (age, Gleason score, preoperative PSA value, immunohistochemical scores for proliferation and p53‐overexpression, 3 stereological parameters of angiogenesis, 2 stereological parameters of epithelial texture). In a stepwise logistic regression analysis with the tumour categories T1a and T1b as dependent variables, only the Gleason score and the volume fraction of epithelial cells proved to be significant as independent predictor variables of the tumour category. Using LVQ and SVM with the information from all 10 input variables, more than 80 of the cases could be correctly predicted as T1a or T1b category with specificity, sensitivity, negative and positive predictive value from 74–92%. Using only the two significant input variables Gleason score and epithelial volume fraction, the accuracy of prediction was not worse. Thus, descriptive and quantitative texture parameters of tumour cells are of major importance for the extent of propagation in the prostate gland in incidental prostatic adenocarcinomas. Classical statistical tools and neuronal approaches led to consistent conclusions.

MULTIPLE DECISION TEMPLATES WITH ADAPTIVE FEATURES FOR FINGERPRINT CLASSIFICATION

2007 ◽  
Vol 21 (08) ◽  
pp. 1323-1338 ◽  
Author(s):  
JUN-KI MIN ◽  
SUNG-BAE CHO
Keyword(s):  
Support Vector Machines ◽  
Feature Vector ◽  
Experimental Results ◽  
Classification Method ◽  
Support Vector ◽  
Fingerprint Classification ◽  
Vector Machines ◽  
Fingerprint Database

This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

Sign in / Sign up

Export Citation Format

Share Document